Rule-based Energy Management Strategy Research Articles

Improved Short-Term Speed Prediction Using Spatiotemporal-Vision-Based Deep Neural Network for Intelligent Fuel Cell Vehicles

In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation.

Model Prediction and Rule Based Energy Management Strategy for a Plug-in Hybrid Electric Vehicle With Hybrid Energy Storage System

This article presents an energy management strategy (EMS) design and optimization approach for a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS) which contains a Li–Ti–O battery pack and a Ni–Co–Mn battery pack. The EMS shares power flows within the hybrid powertrain, and it employs a dual fuzzy logical controller whose inputs are predictions for PHEV powertrain states. An elitist nondominant genetic algorithm using a model in loop simulation approach as fitness functions is implemented to multiobjective optimization for the EMS under worldwide light-duty test cycles. The optimal objectives are improving PHEV mileage, minimizing battery packs capacity fades, reducing HESS degradation inconsistency, and minimizing driving cost unit distance. A hardware in loop test bench has been established to verify EMS performances in embedded systems. The test results under new European driving cycles demonstrate that optimized EMSs remain appropriate for different driving cycles and their performances are close to dynamic programming based offline optimal solutions. Due to the contributions of both the HESS and the optimized EMS, the PHEV energy efficiency has been improved by 1.6%–2.5% and the PHEV energy storage system cycle life can be improved by 159%–203%.

Centralized model predictive control strategy for thermal comfort and residential energy management

A novel implementation strategy of a centralized model predictive control (MPC) is proposed for a zone based comfort and energy management in a residential building. A photovoltaic (PV) solar system and a stationary home battery unit are considered. A heat pump (HP) is used as the primary heating unit. The electric baseboards (BB) are used as a supplementary system. The MPC controls the HP and BB power inputs for comfort management, as well as the energy flow among the PV, the battery and the bidirectional grid system. Variable Time-of-Use (ToU) rates are considered for the energy cost calculation and Feed-in-Tariff (FiT) is considered for selling energy to the grid. A 13.5% reduction in the energy cost is achieved with the centralized MPC as compared to a rule based energy management strategy. The solar energy generation and battery storage contribute to approximately 31% saving.

The Bionics and its Application in Energy Management Strategy of Plug-in Hybrid Electric Vehicle Formation

A novel distributed cooperative formation control method inspired by the aggregate behaviors of fish groups is proposed for plug-in hybrid electric vehicle (PHEV) formation. Firstly, a hierarchical control architecture is established for formation keeping with fuel consumption (FC) optimization simultaneously. The top layer is to generate a leader with the optimal performance based on the nonlinear model predictive control (MPC) technique. A fish swarm optimization (FSO) algorithm is proposed to solve the nonlinear MPC problem by imitating the predation behaviors of the fish swarm. The middle layer is a decentralized intelligent cruise control (ICC) for follower vehicles to track their leader imitated the behaviors of fish swarm, and some design criteria are presented based on the Lyapunov stability theory. The under layer is to achieve a satisfying performance for the hybrid powertrain systems of followers. Finally, the bio-inspired method applied for PHEV formation is verified with a satisfying robustness, fuel economy, car-following and also real-time processing performances. According to the results, the PHEV formation using the proposed method represents a better car-following performance compared with the normal adaptive cruise control (ACC) method and a 21.26% improvement of FC compared with the rule-based energy management strategy (EMS). The computational burden is also reduced by the bio-inspired method.

A Novel Fuel-Cell Electric Articulated Vehicle and Its Drop-and-Pull Transport System

Drop-and-pull transportation can repeatedly utilize tractors with different trailers and reduce costs, carbon emissions, and the number of tractors to purchase and use. Fuel-cell electric vehicles (FCEV) possess high power and long drive endurance. These performance characteristics complement the performance requirements of drop-and-pull transportation of heavy loads and long mileage. This paper proposes a novel fuel-cell electric articulated vehicle featuring three power sources: fuel cell, power battery, and ultracapacitor. Then, based on the proposed vehicle, we expound on a highly efficient and flexible transport system. To compare economics and durability of fuel-cell electric trailers with two energy sources (i.e., fuel-cell-battery) and three energy sources, we developed and simulated a rule-based energy management strategy under driving conditions. The results indicate that, although similar levels of fuel economy and capacity degradation of the fuel cell occur for the proposed vehicle and its two-energy-source counterpart, the ampere–hour throughput of three-energy-source vehicles was 64% lower than that of two-energy-source vehicles, which indicates the introduction of the ultracapacitor in fuel-cell-battery electric articulated vehicles can offer significant protection to the power battery. This result shows that the three energy sources increase the service life of the energy system.

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies

This paper studies the impact of two significant aspects, namely fuel cell (FC) degradation and thermal management, over the performance of an optimal and a rule-based energy management strategy (EMS) in a fuel cell hybrid electric vehicle (FCHEV). To do so, firstly, a vehicle's model is developed in simulation environment for a low-speed FCHEV composed of a FC stack and a battery pack. Subsequently, deterministic dynamic programming (DP), as an optimal strategy, and bounded load following strategy (BLFS), as a common rule-based strategy, are utilized to minimize the hydrogen consumption while respecting the operating constraints of the power sources. The performance of the EMSs is assessed at different scenarios. The first objective is to clarify the effect of FC stack degradation on the performance of the vehicle. In this regard, each EMS determines the required current from the FC stack for two FCs with different levels of degradation. The second objective is to evaluate the thermal management contribution to improving the performance of the new FC compared to the considered cases in scenario one. In this respect, each strategy deals with determining two control variables (FC current and cooling fan duty cycle). The results of this study indicate that negligence of adapting to the PEMFC health state, as the PEMFC gets aged, can increase the hydrogen consumption up to 24.8% in DP and 12.1% in BLFS. Moreover, the integration of temperature dimension into the EMS can diminish the hydrogen consumption by 4.1% and 5.3% in DP and BLFS respectively.

Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability

Abstract This paper presents a real-time and approximately optimal energy management strategy (EMS) based on Pontryagin’s minimum principle (PMP), considering both fuel economy and power source durability. To develop the target strategy, performance degradation models are built for two power sources: a proton exchange membrane fuel cell and a lithium ion battery. This study provides an online co-state updating method for uncertain driving cycles. The method allows the battery’s state of charge to be controlled within a certain range and determines the nearly optimal hydrogen consumption. Furthermore, by incorporating a fuel cell power variation limiting factor with a weight coefficient into the PMP to suppress power changes, the durability of the fuel cell can be improved. The average daily operating cost (calculated based on the fuel consumption and power source degradation) is used to evaluate the trade-off between fuel economy and power source durability. Simulation results show that the fuel cell durability is improved with a slight increase in fuel consumption and battery degradation, and that the average daily operating cost is effectively reduced. A comparison with the results obtained by adopting a rule-based EMS and a dynamic programming-based EMS indicate the superiority of the proposed EMS.

Application of Adaptive Neuro-Fuzzy Inference Rule-based Controller in Hybrid Electric Vehicles

Designing of hybrid architecture has greater importance in the development of electric vehicles to enhance the life cycle of the battery, to protect from nonlinearities and uncertainties of electrical energy storage systems. The objective of this paper is to design and apply the Adaptive Neuro-Fuzzy Inference rule-based controller with the semi-empirical strategy to protect from nonlinearities, uncertainties, and to improve efficiency in electric vehicles. In this paper, a fully active Li-Ion battery/Electric Double-Layer supercapacitor hybrid energy storage system used to decouple Li-Ion battery/Electric Double-Layer Supercapacitor from Direct Current bus and to generate Li-Ion battery current reference online semi-empirical rule-based energy management strategy used. The Control system is designed with the Adaptive Neuro-Fuzzy Interface rule-based controller to reduce non-linearity and different uncertainties of the energy storage system with two outputs battery current and DC bus voltage are chosen to measure control system design, which is tested under heavy and light load conditions. Results are validated using MATLAB/Simulink and the performance of the Adaptive Neuro-Fuzzy Interface rule-based controller is 16.96% and 9.81% greater than Robust Fractional Order Sliding Mode Controller under heavy and light load conditions.

Simulation of a Hybrid Energy System for Stratospheric Airships

Effective energy management systems are critical for long endurance stratospheric airships. This article presents a hybrid energy system consisting of one renewable (photovoltaic) and two energy storage (lithium battery/regenerative fuel cell) systems. After examining different models of energy harvesting, balancing, and storage, we present the details of a rule-based energy management strategy. We further validate it by presenting the various power curves and state of charge (SOC) measurements. The results indicate that the strategy is effective for energy management planning.

A scalable, causal, adaptive rule-based energy management for fuel cell hybrid railway vehicles learned from results of dynamic programming

Abstract A scalable, causal, adaptive rule-based energy management strategy for fuel cell hybrid trains is developed. The rules of this strategy are initiated by the results of two-dimensional dynamic programming under different driving conditions and utilize the convexity of the characteristic specific consumption curve of the fuel cell system. According to the strategy, the fuel cell power follows the estimated average load power. This average value is updated each time when the train leaves a station by using prior knowledge, which ensures its causality. Furthermore, the power demand due to the gradient slope is excluded while estimating the average value because the gravitational energy is recyclable. In this way, the fuel cell system works more stably without being influenced by the strongly changeable power demand due to the gradient slopes. In order to avoid over-charging of batteries during long hold time, which is often the case for regional railway vehicles, the pre-known driving, holding, and travel time available in railway transportation are used to improve the estimation of the average values. After comparison with the results of dynamic programming, an excellent fuel economy is observed under different driving cycles and weather. More consumption of 0.01% and 0.09% in summer and winter, respectively, compared to dynamic programming, results under a typical driving cycle of regional railway vehicles in Berlin. Because the rules are based on the component characteristics, this strategy can be transferred to other vehicle configurations or driving situations without a loss of effectiveness. In addition to the excellent fuel economy, the lifetime of fuel cell systems benefits from its less dynamic operation.

Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship

The fuel cell is generally coupled with the hybrid energy storage system (HESS) to improve power system dynamic performance and prolong the fuel cell lifetime. Therefore, the sizing of HESS and design of energy management strategy (EMS) have already become key research points. Based on support vector machine and frequency control, a novel EMS is proposed. As the sizing of HESS and the design of energy management strategy have a strong inner link, a multi-objective optimization method for the HESS and EMS is proposed. After that, simulations are used to compare the performance of the optimal hybrid power system. Compared with the different hybrid power system structures, the optimal HESS can meet power demand and reduce the cost of the energy storage device. Compared with the rule-based energy management strategy, the energy consumption of the optimal hybrid power system reduces 5.4%, and improves power quality and prolongs the device life. The results indicate that the proposed method can achieve excellent performance and is easily applied.

A Hierarchical Energy Management for Hybrid Electric Tracked Vehicle Considering Velocity Planning With Pseudospectral Method

This article proposes a hierarchical energy management strategy (EMS) for hybrid electric tracked vehicle (HETV) considering the two tracks velocity planning based on pseudospectral method (PM). Constrained by the reference path known a priori , the upper layer of the hierarchical EMS finds the optimal velocity of the two tracks, in which the two motor torques are chosen as the control variable to minimize an objective function, trading off the energy consumption, and path tracking accuracy. Based on the obtained optimal velocity profile, the lower layer distributes the power demand to the engine-generator and the battery to minimize the energy consumption. The hierarchical EMS is designed to minimize energy consumption while ensuring the premise of the vehicle path tracking performance. Both layers adopt the PM which transforms the optimal control problem (OCP) into nonlinear programming (NLP) problem, and the Sparse Nonlinear OPTimizer (SNOPT) solver is used. Simulation results show that the fuel economy of the PM outperforms that of dynamic programming (DP). Compared with DP, the hierarchical EMS can save fuel consumption by 3.92% with a significantly reduced computation burden. Finally, field experiments show that the proposed method improves fuel economy by 14.85% compared with the rule-based EMS without velocity optimal planning.

Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

The traditional vehicle power supply is unable to meet the power requirement of electric power steering system (EPS) in heavy-duty vehicles at low speeds. A novel EPS with hybrid power supply (HP-EPS) is constructed in this paper, and a new optimized rule-based energy management strategy of hybrid power supply system is designed. The strategy determines the power distribution of the vehicle power supply (VPS) and super capacitor (SC), as well as the charging or discharging of SC. Furthermore, to minimize the output current fluctuation of the VPS, the optimization model of parameters in the strategy is established and the particle swarm optimization algorithm (PSO) algorithm is applied to optimize the rules in the energy management strategy. The verification for the designed energy management strategy is carried out in MATLAB/Simulink and results show that the output current peak of VPS decreases by 33% and its fluctuation depresses significantly. In addition, the SC is charged timely and fast, which is beneficial to guarantee enough state of charge (SOC) of SC. In conclusion, the optimized rule-based energy management strategy used for the HP-EPS system can meet the current requirement of EPS and effectively reduce the peak and fluctuation of the VPS output current.

MPC-Based Energy Management Strategy for an Autonomous Hybrid Electric Vehicle

Despite the current intense research on each of the subjects of electrification and autonomous driving, potential advantages as a result of the interaction of these two mainstreams in automotive have not been effectively studied yet. Autonomous vehicles generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. In this article, we present a novel approach for improving the fuel economy of an autonomous hybrid electric vehicle by taking advantage of this qrydata. We introduce the term of autonomous-specific energy management strategy (ASEMS) and we present an example of such a strategy using model predictive control (MPC). Specifically, we show how a more fuel-optimal energy management strategy (EMS) can be achieved for the power-split powertrain of an autonomous hybrid electric vehicle using the motion planning data. We use an optimization-based motion planning approach and feed the resulting velocity profile up to the prediction horizon to the MPC-based EMS. The presented approach shows 2% to 12.81% less fuel consumption for the two extreme cases of 100 and 1000 meters as the prediction horizons, compared to a rule-based EMS. The presented EMS fuel-optimality for the 1000 meters is only 6.91% sub-optimal compared to the globally optimal results of dynamic programming.

Adaptive Rule-Based Energy Management Strategy for a Parallel HEV

This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium- and heavy-duty vehicles. ARBS can be deployed online to optimize fuel consumption without any detailed knowledge of the engine efficiency map of the vehicle or the entire duty cycle. The proposed strategy improves upon the established Preliminary Rule-Based Strategy (PRBS), which has been adopted in commercial vehicles, by dynamically adjusting the regions of operations of the engine and the motor. It prevents the engine from operating in highly inefficient regions while reducing the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink®, both the proposed ARBS and the established PRBS strategies are compared over an extended duty cycle consisting of both urban and highway segments. The results show that ARBS can achieve high MPGe with different thresholds for the boundary between the motor region and the engine region. In contrast, PRBS can achieve high MPGe only if this boundary is carefully established from the engine efficiency map. This difference between the two strategies makes the ARBS particularly suitable for aftermarket hybridization where full knowledge of the engine efficiency map may not be available.

Research on the Impact of Electricity Price Fluctuation on Energy Management Strategy

Energy management strategy (EMS) is an effective measure to improve the energy economy, however, where fluctuant electricity and fuel prices were not taken into account. Since price fluctuations have an impact on rule-based energy management strategies, constant price energy management strategies are not optimal. This paper proposes a novel energy management strategy considering price fluctuation of electricity and fuel for plug-in electric vehicles (PHEVs). Firstly, the electricity price data of partial cities during peak and trough periods are counted. Secondly, a SA-PSO algorithm is used in the rule-based energy management strategy to optimize the key thresholds. The influence of electricity price fluctuation on energy management strategy has been analyzed.

A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity

In order to enhance energy efficiency and improve system performance, the road mobility system requires more preview information and advanced methods. This paper proposes a novel hierarchical optimal energy management strategy for electric buses with a battery/ultracapacitor hybrid energy storage system, to optimal split the power and reduce the battery life degradation. This method is based on vehicle-to-cloud connectivity. In the cloud platform, an optimal energy management strategy is developed using dynamic programming, where the battery degradation cost and the electric cost are taken into consideration. In the vehicle level, a model predictive control is developed to deal with the uncertainties, reduce the energy losses, and handle the system constraints. The cost function of the model predictive control includes the ultracapacitor state of charge planning and energy losses. In order to evaluate the effectiveness of the proposed method, a rule-based energy management strategy is developed as the baseline approach. The China bus driving cycle and other six real bus driving cycles recorded in China are used to validate the robustness of the proposed method. To be more realistic, the random uncertainties up to 20% are included in all driving cycles. Furthermore, the time delay and packet losses in communication are also considered. Simulation results show that the proposed method significantly outperforms the rule-based method, and the average improvement could be over 40% in the studied driving cycles.

Energy management strategy for electric vehicles based on deep Q-learning using Bayesian optimization

In this paper, a deep Q-learning (DQL)-based energy management strategy (EMS) is designed for an electric vehicle. Firstly, the energy management problem is reformulated to satisfy the condition of employing DQL by considering the dynamics of the system. Then, to achieve the minimum of electricity consumption and the maximum of the battery lifetime, the DQL-based EMS is designed to properly split the power demand into two parts: one is supplied by the battery and the other by supercapacitor. In addition, a hyperparameter tuning method, Bayesian optimization (BO), is introduced to optimize the hyperparameter configuration for the DQL-based EMS. Simulations are conducted to validate the improvements brought by BO and the convergence of DQL algorithm equipped with tuned hyperparameters. Simulations are also carried out on both training dataset and the testing dataset to validate the optimality and the adaptability of the DQL-based EMS, where the developed EMS outperforms a previously published rule-based EMS in almost all the cases.

Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin's Minimal Principle

The fuel cell hybrid vehicle provides an efficient and low-emission alternative for the internal combustion engine vehicle. The energy management strategy (EMS) commands the power split between the power sources and is crucial to the hybrid vehicles. In this work, we propose an adaptive EMS based on Pontryagin's Minimal Principle for a fuel cell/battery hybrid vehicle, in which the co-state adaptation is performed by driving cycle prediction. In order to improve the co-state estimation accuracy, an improved Markov based velocity prediction is proposed considering the driving behavior under different driving patterns. Moreover, the driving pattern is recognized online based on a support vector machine method, which is optimized by particle swarm optimization. We build a combined driving cycle to verify the effectiveness of the proposed strategy, simulation results under three cases show that the proposed strategy can foresee the driving behaviors and update the co-state reasonably. Comparing with the rule-based EMS, the proposed strategy achieves a better fuel economy with a hydrogen consumption reduction by 4% and a relatively low average power change rate of fuel cells. Moreover, it achieves a close performance to the offline optimal algorithms.