Adaptive Equivalent Consumption Minimization Strategy Research Articles

A New Real-Time Optimal Energy Management Strategy for Parallel Hybrid Electric Vehicles

Based on the equivalent consumption minimization strategy (ECMS), a novel real-time energy management (EM) strategy for parallel hybrid electric vehicles (HEVs) is introduced. Given the full trajectory of the driver demanded power, the ECMS optimal equivalent factor $\lambda ^{*}$ can be determined. For causal EM strategies, the entire drivecycle is not known in advance. Thus, adaptive ECMS (A-ECMS) was introduced, which sets the time-varying equivalent factor $\lambda$ as an estimate of $\lambda ^{*}$ . The proposed EM strategy is an A-ECMS. This EM strategy is designed to catch energy-saving opportunities (CESOs) during the trip, and thus, it is named ECMS-CESO. Since ECMS-CESO eliminates the calculations used for predicting the vehicle velocity and performing horizon optimization, it is easy to implement and fast for real-time applications. Simulation results show that ECMS-CESO yields fuel economy (FE) close to the maximum FE. Compared with an A-ECMS, the proposed strategy improves FE by 7%.

Experimental Research on Adaptive Equivalent Consumption Minimization Strategy Based on Traffic Information

Experimental Research on Adaptive Equivalent Consumption Minimization Strategy Based on Traffic Information

Real‐time energy management for commute HEVs using modified A‐ECMS with traffic information recognition

To further improve fuel consumption performance of hybrid electric vehicles (HEVs) running on commute route in the face of time-varying traffic information, this paper investigates a real-time energy management strategy based on the adaptive equivalent consumption minimization strategy (A-ECMS) framework with traffic information recognition. The proposed management strategy integrates the global near optimization and the real-time performance. The simple traffic recognition is constructed by utilising k-means clustering algorithm to deal with the historical traffic data to form four clusters. The adaptive equivalence factor of the A-ECMS is designed as a three-dimensional mapping on each cluster and the system states by employing stochastic dynamic programming (SDP) policy iteration to solve offline the stochastic optimal control problem formulated by each cluster statistical characteristic. In real-time energy management controller online, the instantaneous power split is performed by the ECMS with a proper equivalent factor, which is obtained from mappings according to the cluster recognised by the current traffic situation and the state-of-charge (SOC). The effectiveness of the designed control strategy is verified by the simulation test conducted on GT-suite HEV simulator over real driving cycles.

An Adaptive Equivalent Consumption Minimization Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Balance Principle

Energy management strategies can directly determine the dynamic performance and fuel economy of plug-in hybrid electric vehicles (PHEVs). In this paper, an adaptive equivalent consumption minimization strategy (A-ECMS) is proposed based on the energy balance principle of the hybrid powertrain of the target vehicle, by which a pair of boundary equivalent factors can be determined according to the future transportation information. Then, the equivalent factor is calculated in real time based on the energy variation in the powertrain system during the operation. Consequently, the torque distribution between the engine and the motor can be determined by solving the Hamilton function according to the dynamically adjusted equivalent factor, and thus, the energy management control is adaptively realized. The simulations were conducted considering three typical driving conditions, different battery aging statuses, and inaccurate road information. The results manifest that the proposed algorithm is feasible to improve the fuel economy with attainable adaptivity and robustness compared with the typical ECMS.

Estimation of the ECMS Equivalent Factor Bounds for Hybrid Electric Vehicles

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.

An improved adaptive equivalent consumption minimization strategy for parallel plug-in hybrid electric vehicle

Energy management strategies are of the vital importance in fully playing the potential of plug-in hybrid electric vehicles. This paper proposes an improved adaptive equivalent minimization strategy for a parallel plug-in hybrid electric vehicle. In this method, performance of adaptive equivalent minimization strategy is prompted through incorporating information of future driving condition into equivalent factor adjustment. Two main works have been done. First, a novel equivalent factor adjustment method is proposed in the improved adaptive equivalent minimization strategy. Based on the predicted information of future driving condition, equivalent factor is tuned in each running loop of equivalent minimization strategy. Second, information of future driving condition is predicted by harnessing floating car data. Benefit from future driving condition forecasting, the improved adaptive equivalent minimization strategy holds better capability in accommodating road condition switching and preliminary self-regulation for hilly road. Simulation results show that, compared with the convention adaptive equivalent minimization strategy, the improved adaptive equivalent minimization strategy can obtain better fuel economy.

Hierarchical energy management control strategies for connected hybrid electric vehicles considering efficiencies feedback

This paper presents a closed-loop hierarchical energy management control strategy for a group of connected hybrid electric vehicles (HEVs) considering the feedback of propulsion and recuperation efficiencies. The higher level controller utilizes model predictive control (MPC) incorporating signal phase and timing (SPAT) information for the prediction of the optimal velocity profile over a finite time horizon, with periodically updated efficiencies from the lower level controller. The adaptive equivalent consumption minimization strategy (A-ECMS)-based lower level controller then makes use of the optimal velocity profile for the energy management control of the HEVs. Meanwhile, the lower level controller calculates and feeds back the average propulsion and recuperation efficiencies of the current time window to the higher level controller, which updates the efficiencies with the average values and corrects the fuel consumption model for the calculation of the velocity profile of the next time window. Simulation results validate the control effectiveness of the proposed control strategy.

Driving-Style-Oriented Adaptive Equivalent Consumption Minimization Strategies for HEVs

The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers’ driving style. This paper proposes a driving-style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, first, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on energy management strategies are discussed by analyzing the performance of the equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS.

Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction

A major advantage of plug-in hybrid electric vehicles is their high fuel economy, which is closely related to their energy management strategy and driving cycles. In this study, an improved velocity prediction method is formulated based on the Markov model and a back propagation neural network. The root mean square error of the predicted velocity for the New European Driving Cycle is 0.1511 m/s when the prediction time is 3 s. Moreover, a vehicle test for the velocity prediction algorithm is implemented on a hybrid electric bus, which verifies the reliability and real-time performance. On this basis, a model predictive control-based energy management strategy incorporating the velocity prediction is proposed. In order to lessen the computation and memory burden and constrain the battery's state of charge, a state of charge-based adaptive equivalent consumption minimization strategy is applied to the predictive control-based energy management strategy. By simulation, the proposed velocity prediction-based energy management strategy improves fuel economy by 3.11% and 7.93% for a plug-in hybrid electric commercial vehicle in the New European Driving Cycle, while 2.96% and 11.02% in the Worldwide Harmonized Light Vehicles Test Procedures, when compared with the adaptive equivalent consumption minimization strategy and equivalent consumption minimization strategy, respectively.

Comparative Study of Real-Time HEV Energy Management Strategies

An adequate energy management strategy is the key to optimizing hybrid electric vehicle fuel efficiency. Various real-time controls have been recently developed. As each study is performed in a specific context, a comparative analysis is critically needed to point out their pros and cons. This paper proposes a comparison between three promising real-time strategies: adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL), and stochastic dynamic programming (SDP). Two offline algorithms are used as benchmark: Pontryagin's minimum principle and dynamic programming. Implementation and parameters setting issues are discussed for each strategy. The real-time strategies robustness is then evaluated over several types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation results show that OCL needs improvement. A-ECMS reaches the best fuel saving performance when used with parameter sets adjusted to the driving environment, while SDP better respects the charge sustaining constraint.

Near-Optimal SOC Trajectory for Traffic-Based Adaptive PHEV Control Strategy

The optimal charge and discharge management of the energy storage system (ESS) has a pivotal role in development of the energy management system for the plug-in hybrid electric vehicles (PHEV). This research aims to estimate a near optimal trajectory of ESS state of charge (SOC) to be employed in traffic-based adaptive PHEV control strategy to enhance fuel economy in various driving cycles. In the first step, several driving cycles are developed based on the real-world driving data. Experimental map-based Pontryagin's minimum principle control strategy is designed to extract the optimal SOC trajectories for different cycles. The achieved optimal data is then used to create the database employed for training a Nero-Fuzzy system. The SOC trajectories, estimated under the light of the obtained fuzzy inference system, are employed as the reference SOC profiles in adaptive equivalent consumption minimization strategy. The simulation results reveal that the proposed approach provides near optimal SOC trajectory and improves the equivalent fuel economy in various driving cycles. Moreover, this adaptive near optimal control strategy could be applied on-line as it doesn't require the knowledge of the entire driving cycle, and only the trip length and the average speed should be known a priori.

Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles

This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion mode and then switch to the charge-sustaining mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the charge-sustaining mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average.

Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction

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.

Predictive AECMS by Utilization of Intelligent Transportation Systems for Hybrid Electric Vehicle Powertrain Control

Information obtainable from intelligent transportation systems (ITS) provides the possibility of improving safety and efficiency of vehicles at different levels. In particular, such information also has the potential to be utilized for the prediction of driving conditions and traffic flow, which allows Hybrid Electric Vehicles (HEVs) to run their powertrain components in corresponding optimum operating regions. This paper proposes to improve the performance of one of the most promising realtime powertrain control strategies, called adaptive equivalent consumption minimization strategy (AECMS), using predicted driving conditions. In this paper, three real-time powertrain control strategies are proposed for HEVs, each of which introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. These factors are proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy, number of engine transients (ON/OFF), and charge sustainability of the battery.

A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin’s minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.

An Online Rolling Optimal Control Strategy for Commuter Hybrid Electric Vehicles Based on Driving Condition Learning and Prediction

This paper presents an online supervisory control strategy for commuter hybrid electric vehicles (HEVs) based on driving condition learning and prediction. The aim is to provide an online self-learning-based framework to keep Pontryagin's minimum principle (PMP)-based control adapting to the time-varying driving condition on commuting routes and minimize fuel consumption. There are three steps to realizing this strategy. First, two novel statistical features are proposed to describe the frequency distribution of achievable working points of the hybrid powertrain under a driving condition. Second, based on the characteristic that commuting trips with similar trip start time, direction, and weather condition have similar driving conditions, we develop an instance-based machine learning algorithm to learn the driving condition. A k -nearest neighbor ( k -NN) prediction algorithm is used to predict future driving conditions. Third, we establish an online supervisory control strategy, together with rolling driving condition prediction and optimal costate approximation. The approximation algorithm can approximate the optimal constant costate for the entire prediction horizon just based on the proposed two features. Simulation study and bench tests are conducted on a parallel hybrid powertrain of a city bus using standard driving cycles and real-world sampled commuting trips with different trip start times and weather conditions. The results show that the proposed driving condition learning and prediction method is effective for predicting the upcoming driving conditions. Meanwhile, the proposed strategy shows substantial improvement of fuel economy compared with the rule-based (RB) strategy and the adaptive equivalent consumption minimization strategy (A-ECMS).

Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads

This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper for every HEV, where the higher level and the lower level controller share information with each other and solve two different problems that aim at improving its fuel efficiency. The higher level controller of each HEV is considered to utilize traffic light information, through vehicle to infrastructure (V2I) communication, and state information of the vehicles in its near neighborhood, via vehicle to vehicle (V2V) communication. Apart from that, the higher level controller of each HEV uses the recuperation information from the lower level controller and provides it the optimal velocity profile by solving its problem in a model predictive control framework. Each lower level controller uses adaptive equivalent consumption minimization strategy (ECMS) for following their velocity profiles, obtained from the higher level controller, in a fuel efficient manner. In this paper, the vehicles are modeled in Autonomie software and the simulation results are provided in the paper that shows the effectiveness of the proposed control architecture.

State of Charge Management for Plug-In Hybrid Vehicles With Uncertain Trip Information

Efficient state of charge management of plug-in hybrid electric vehicles (PHEVs) differs from their nonplug-in counterparts through the utilization of a charge depleting (CD) mode of operation. Several studies have shown that a blended mode of CD holds fuel economy advantages over a CD and charge sustaining (CS) combination, however, these approaches assume knowledge of the total journey distance. Here, this assumption is relaxed and the state of charge trajectory was recalculated online using a weaker assumption that only a probability distribution accumulated over past trips is available. The importance of other contributing factors to the state of charge profile such as vehicle velocity and altitude is also assessed. Simulation results on a prototype plug-in hybrid are presented with an adaptive equivalent consumption minimization strategy (ECMS) used by the powertrain management to track the proposed state of charge trajectory. The financial and environmental benefits of the proposed approach relative to other state of charge management strategies are then calculated over a number of different cycles and conditions.

A Comparative Analysis of Route‐Based Energy Management Systems for Phevs

AbstractPlug‐in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero‐emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A‐ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine‐tuned for the Toyota Prius plug‐in hybrid powertrain and their performances are evaluated by using a high‐fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A‐ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A‐ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.

A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview

This paper presents a supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. The aim is to minimize the fuel consumption in real-time operation. This strategy is realized through three successive steps. First, a neural network model is established to predict the energy demand of the vehicle. It reduces the complete traffic data to several statistical parameters, which contributes to ease the prediction process. Second, a mathematical model is proposed to translate the predicted energy demand into a state of charge (SOC) reference of the battery, which significantly simplifies the SOC-programming method. Finally, the adaptive equivalent consumption minimization strategy (ECMS) is used to track the SOC reference and determine the powertrain state. The proposed strategy can optimally distribute the energy between the engine and the motor on a global range and achieve an optimal torque split on a local range. Simulations are carried out on a power-split plug-in hybrid electric bus, and the proposed strategy shows substantial improvements in fuel economy and other indexes compared with the rule-based strategy and the ECMS.