Global Energy Management Strategy Research Articles

Efficient Multidimensional Dynamic Programming-Based Energy Management Strategy for Global Composite Operating Cost Minimization for Fuel Cell Trams

Energy management is key to improve the performance of hybrid system. At present, the global optimization-based energy management strategy (EMS) is considered to have the best optimization effect under a known operating condition. However, the traditional global optimization-based EMS is often very time-consuming, which brings great inconvenience to debug and obtain the optimal result. Besides, in the literature, most works still introduce only the fuel consumption into the objective function while failing to consider the durability of fuel cells. In order to make up for these shortcomings, based on the modeling of hybrid system, this article first proposes a global composite operating cost minimization strategy (CMS) both considering fuel consumption and depreciation of purchase cost of fuel cells. Then, this article proposes computationally efficient multidimensional dynamic programming (EMDP) to resolve the optimal control trajectory of the studied issue. Finally, based on the hardware-in-the-loop platform, this article carries out comparative tests of the proposed EMDP algorithm and CMS strategy with traditional ones. Results show that the EMDP algorithm can effectively speed up the computation compared with the traditional dynamic programming. Besides, the proposed CMS strategy would lead to lower operating cost mainly by reducing the degradation cost of fuel cells so that the lifetime of fuel cells could also be extended.

Energy management strategy of four-wheel drive SUV electric-hydraulic hybrid (EHH) power system based on optimal instantaneous efficiency

To address the problem of a large inrush current in pure electric vehicles under high-power driving and braking conditions, which damages the cycle life of the battery, a hydraulic auxiliary drive system is used to design a four-wheel-drive sport utility vehicle (SUV) electric–hydraulic hybrid(EHH) power system. Energy management strategy (EMS) research on this EHH power system is conducted. An EMS based on optimal instantaneous efficiency is proposed. The simulation results under the China light-duty vehicle test cycle for passenger car (CLTC-P) show that control effect of the EMS can improve by 3.27% than the EMS based on rule and achieve 97.43% of control effect of the dynamic programming(DP) global optimal EMS in terms of economic performance. Then the model predictive control (MPC) method is used, and the instantaneous efficiency optimal EMS is optimized. The simulation results under the CLTC-P show that the instantaneous efficiency optimal EMS optimized by MPC has an economic performance improvement of 1.13% compared with the performance before optimization. It can improve by 4.36% than the EMS RB and achieve 98.54% of control effect of the dynamic programming(DP) global optimal EMS in terms of economic performance.

Energy management strategy integrating self‐adaptive adjustment and Pontryagin's minimum principle‐based optimization for fuel‐cell hybrid electric vehicle

AbstractA hierarchical energy management strategy (EMS) integrating self‐adaptive adjustment and Pontryagin's minimum principle‐based optimization is proposed for a fuel cell hybrid electrical vehicle. First, the proposed EMS estimate the future power requirement by using Markov chain Metropolis–Hastings sampling, second the parameters of the low‐pass filter is adjusted according to the state of charge estimation of the super capacitor, finally the best fuel economy is realized through Pontryagins minimum principle‐based optimization. The evaluation of the EMS is verified via simulation and real driving cycle, the results are compared with another global optimization EMS. The results show that the proposed EMS can fully exert the function of the super capacitor and improve the fuel economy 0.3% and 8.8% for simulation and real driving cycle, respectively.

Adaptive real-time optimal control for energy management strategy of extended range electric vehicle

The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programming-based global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13–23 times higher than that of the shooting method.

Cloud computing-based real-time global optimization of battery aging and energy consumption for plug-in hybrid electric vehicles

This paper addresses two conflicts in the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). One is the conflict between fuel economy optimization and battery state of health preservation, and the other is the conflict between global optimality and real-time capability. Inspired by the hierarchy structure of a computer, a two-layer internet-distributed EMS (ID-EMS) is developed using cloud computing and the internet of vehicles. The top layer in the cloud, which possesses powerful calculating capability, focuses on global optimality by utilizing machine learning technology and stochastic dynamic programming. The bottom layer on board, with limited computing power, employs a fuzzy controller to respond to real-time conditions while trying not to deviate too far from the global solution. Thus, a real-time global optimal EMS can be achieved. The ID-EMS is implemented on an internet-distributed vehicle-in-the-loop simulation platform whose in-loop vehicle makes it possible to test the ID-EMS in an on-road driving experiment. The ID-EMS outperforms a rule-based EMS in terms of overall cost by 6.8%, but it is surpassed by an acausal EMS with dynamic programming by 7%. These results suggest directions for the future development of EMS for PHEVs using cloud computing.

Fail-Safe Power for Hybrid Electric Vehicles: Implementing a Self-Sustained Global Energy Management System

This article studies the energy management of a hybrid electric vehicle (HEV) equipped with a battery, an ultracapacitor system (UCS), and a fuel cell system (FCS). The energy management strategy (EMS) is designed to enable degraded operation should an energy source fail. In the considered architecture, the vehicle is not operational without a battery because it imposes the dc bus voltage; therefore, degraded operations are only considered for failures in the FCS or UCS. The objective is not to develop a new EMS for each degraded operation mode but, rather, to slightly modify the global EMS developed for normal operation. The article presents simulation and experimental validation results.

An On-Line Energy Management Strategy Based on Trip Condition Prediction for Commuter Plug-In Hybrid Electric Vehicles

This paper presents an on-line energy management strategy (EMS) based on trip condition prediction for commuter plug-in hybrid electric vehicles (P-HEVs). The purpose is to provide an on-line predictive control approach to minimize fuel consumption. Two pivotal contributions are provided to realize the purpose. First of all, we establish the trip condition prediction model by using back propagation neural network, to obtain the real-time vehicle-speed trajectory on-line. Particularly, both the genetic algorithm and particle swarm optimization algorithm are applied to improve the prediction accuracy of the trip condition prediction model. Next, to obtain an applicable EMS in real time, we propose a dynamic programming-based predictive control strategy. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Beijing road network. The results show that the designed trip condition prediction model can effectively realize the on-line vehicle-speed prediction, and the prediction accuracy is more than 93%. In addition, compared to the offline global optimization EMS, although the proposed strategy makes the fuel consumption grow less than 5.2%, it can be implemented in real time. Moreover, compared with the existing real-time EMSs, it can further reduce the fuel consumption and emissions. It shows that the proposed EMS can provide an effective solution for commuter P-HEVs applying it on-line.

Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles

The energy management strategy (EMS) is a key to reduce the equivalent hydrogen consumption and slow down fuel cell performance degradation of the plug-in fuel cell hybrid electric vehicles. Global optimal EMS based on the whole trip information can achieve the minimum hydrogen consumption, but it is difficult to apply in real driving. This paper tries to solve this problem with a novel hierarchical EMS proposed to realize the real-time application and approximate global optimization. The long-term average speed in each future trip segment is predicted by KNN, and the short-term speed series is predicted by a new model averaging method. The approximate global optimization is realized by introducing hierarchical reinforcement learning (HRL), and the strategy within the speed forecast window is optimized by introducing upper confidence tree search (UCTS). The vehicle speed prediction and the proposed EMS have been verified using the collected real driving cycles. The results show that the proposed strategy can adapt to driving style changes through self-learning. Compared with the widely used rule-based strategy, it can evidently reduce hydrogen consumption by 6.14% and fuel cell start-stop times by 21.7% on average to suppress the aging of fuel cell. Moreover, its computation time is less than 0.447 s at each step, and combined with rolling optimization, it can be used for real-time application.

Optimized energy management control for the Toyota Hybrid System using dynamic programming on a predicted route with short computation time

Among the general problematic of the HEV power trains, the most critical point is the determination of the power-split ratio between the mechanical and the electrical paths, known as the energy management strategy (EMS). Many EMS are proposed in the literature, and can be grouped in two categories: the local optimization EMS and the global optimization EMS. The local optimization category corresponds to the EMS based on human expertise and the knowledge of the power train components efficiency maps. Thus, the local optimization EMS manages the power train operations by referring to predefined rules. The drawback of such strategies is that it brings an instantaneous fuel consumption optimization, and does not fully optimize the fuel consumption over the whole trip. Therefore, additional fuel savings are still possible. This paper presents an overall optimized predictive EMS for the Toyota Hybrid System (THS-II) power train of the Prius. The proposed EMS is based on Dynamic Programming (DP), where the prior knowledge of the route is required in order to predetermine the power-split ratio and optimize the fuel consumption for the whole predicted route. The DP EMS proposed for the THS-II power train is designed with a very short computation time, intended to be implemented in real-time applications. The potential of this DP-controller in reducing fuel consumption on regulatory cycles are computed and compared to a rule-based controller and to the Prius published fuel consumption results. Finally, the fuel reduction enhancements of the DP-controller are computed for real road tests achieved on a MY06 Prius in Ile-de-France, by comparing to the associated observed consumption measurements.

Energy Storage and Management System With Carbon Nanotube Supercapacitor and Multidirectional Power Delivery Capability for Autonomous Wireless Sensor Nodes

This paper presents an energy storage and management system to achieve long lifetime and miniaturization for autonomous wireless sensor nodes, which can be used in communication network for microgrids. The system employs supercapacitors to form a multienergy-source structure, and thus features multidirectional power delivery capability, which in turn allows the implementation of such state-of-the-art power management techniques as dynamic voltage scaling (DVS). A global energy management strategy is introduced to realize appropriate energy delivery, with the aid of a power management unit consisting of several proposed power converters that are capable of bidirectional operation. The bidirectional operation also dramatically increases the tracking speed during DVS with a charge-recycle technique. Fabrication of supercapacitor featuring compatibility with the CMOS process is also discussed, focusing on the preparation of free-standing single-walled carbon nanotube (CNT) films directly on a Si substrate, as electrodes for supercapacitor. A prototype of a dc-dc converter experimentally verifies the bidirectional operation and an improvement of over 30 times on tracking speed during DVS. Meanwhile, experiments on a CNT supercapacitor coin cell show high performances and excellent stability. The proposed designs provide the possibility of a fully on-chip energy system with the concept of heterogeneous integration.