Development Of Energy Management Strategy Research Articles

Driving Cycle-Based Energy Management Strategy Development for Range-Extended Electric Vehicles

<div>Environmental concerns and technological progress push the development and market penetration of electric vehicles (EVs) and hybrid electric vehicles (HEVs). On the other hand, transportation systems are becoming more efficient by improved communication systems within vehicles and between vehicles and infrastructure. In this study, a driving cycle-based energy management strategy is developed for range-extended electric vehicles (REEVs) to increase system efficiency and equivalent vehicle range. A validated vehicle model is developed by critical subsystem testing and a comparative study is conducted to assess the developed strategy. The results showed that the optimized strategy can save CO<sub>2</sub> emission by 6.21%, 1.77%, and 0.58% for heavy, moderate, and light traffic, respectively. Furthermore, the efficient use of a range extender (REx), guided by traffic data, extends the vehicle range, especially in heavy traffic conditions.</div>

Prediction of cold start emissions for hybrid electric vehicles based on genetic algorithms and neural networks

The emission of hybrid electric vehicles deteriorates during cold start, and it is a cost-effective method to reduce pollutant emissions during cold start of hybrid electric vehicles through energy management strategies. The development of energy management strategies for hybrid electric vehicle requires an accurate cold-start emissions model. However, traditional cold-start emission models based on statistics and experience are difficult to achieve satisfactory prediction accuracy, and require a lot of experimental cost and time. The use of neural networks for cold start emission prediction of hybrid electric vehicles has been widely used because of its low cost and high prediction accuracy. In this paper, the genetic algorithm is used to optimize the weights and thresholds of the neural network, and a neural network cold-start emission prediction model for hybrid electric vehicles is built. We trained the neural network using the high temperature (40 °C), normal temperature (23 °C) and low temperature (−20 °C) cold start data of hybrid vehicles obtained in the World Light Vehicle Test Code (WLTC), studied the cold start prediction results under the different cold start temperature, studied the cold start prediction results under the different input parameters, studied the cold start prediction results of the neural network optimized by the genetic algorithm. Under different input parameters and cold start temperature, the GA-BP algorithm can accurately predict the emissions of carbon monoxide (CO), carbon dioxide (CO2), hydrocarbons (THC), nitrogen oxides (NOx) and particulate matter number (PN) under hybrid electric vehicles.

Stochastic optimisation-based methods for the identification of the physical parameters of a metal hydride tank

In this paper, three optimisation methods are used to determine the parameters of a commercial metal hydride hydrogen tank, considered as a grey box in which the thermophysical parameters are unknown. These parameters are essential for the development of energy management strategies and the study of tank ageing. A model of the hydride tank containing an AB5 intermetallic was developed and experimentally validated. An absorption test campaign of the studied tank was carried out to collect data necessary for the optimisation algorithms. A comparative study was carried out in accordance with the speed of convergence and precision between the three stochastic optimisation methods: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). The results of the simulation of the storage system using the parameters determined by the algorithms show a remarkably close agreement with the experimental data, with a maximum error of 2.1%.

Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio

The innovation and development of energy management strategies attract more and more attention as a key technology in hybrid electric vehicles. This paper focuses on a novel type of electric-hydraulic hybrid vehicle with multiple working modes and zero emissions, which offers a deeper potential for energy efficiency. Steady-state simulation with a rule-based mode switching strategy verifies that the electric-hydraulic ratio in the hybrid driving mode can interfere with energy management performance. Committed to filling the literature gap on the electric-hydraulic ratio, this paper proposes the idea of combining deep reinforcement learning with a rule-based control strategy and employs the Twin Delayed Deep Deterministic Policy Gradient to control the electric-hydraulic ratio. Thereafter, an energy management strategy framework based on the self-adaptive electric-hydraulic ratio was developed. Offline training and online test can demonstrate that the proposed energy management strategy enables the self-adaptive electric-hydraulic ratio under various driving cycles and a significant reduction in the energy consumption rate. The research findings in this paper are the crystallization of traditional control strategy and advanced algorithm, which has stronger practical value and development prospects. This is the first time that the self-adaptive electric-hydraulic ratio is applied to the establishment of energy management strategies.

Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle

The configuration and development of energy management strategies (EMSs) are generating considerable interest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV.

A Framework for States Co-Estimation of Hybrid Energy Storage Systems Based on Fractional-Order Theory

Accurate state estimation is crucial to the control and management of the hybrid energy storage system (HESS). Although there have been many studies on state estimation of lithium-ion batteries (LIBs) and ultracapacitors (UCs), state estimation for HESS is scarce and poorly defined. To fill this gap, this article aims to propose a co-estimation framework of the state-of-charge (SoC) and remaining discharging time (RDT) for the HESS based on fractional-order theory. First, considering the impedance characteristics of the LIB and UC, the electrochemical impedance spectroscopy (EIS) experiment is conducted to establish the fractional-order models (FOMs). The grey wolf optimizer (GWO) is used to identify the model parameters. Then the fractional extended Kalman filter (FEKF) is used to obtain the SoC for the LIB and UC. A statistical method with the forgetting factor is used for load current forecasting. Finally, the co-estimation framework of the SoC and RDT for the HESS is presented and verified with experimental data. The root-mean-square error (RMSE) of the SoC estimation for the HESS is less than 0.92% in two cases. The experimental results indicate that the proposed co-estimation method of the HESS has achieved high precision and a fast convergence rate under the typical current working condition. This work can be used for a better energy management strategy development.

Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach

The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO2 and pollutant emissions (CO, NOx and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution.The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions.

Non-Linear Programming-Based Energy Management for a Wind Farm Coupled with Pumped Hydro Storage System

The large-scale integration of renewable energy sources (RESs) has become one of the most challenging topics in smart grids. Indeed, such an integration has been causing significant grid stability issues (voltage and frequency control) due to the dependency of RESs on meteorological conditions. To this end, their integration must be accompanied by alternative sources of energy to attenuate the power fluctuations. Energy storage systems (ESSs) can provide such flexibility by mitigating local peaks/drops in load demands/renewable power generation. Therefore, the development of energy management strategies (EMSs) has been attracting considerable attention in the management of the power generated from the RESs associated with that which is stored/provided by the ESSs. Then, the optimization of the EMS leads to substantial savings in operation and maintenance and to correct decisions for the future. This study presents an optimized EMS for a wind farm, coupled with a pumped hydro energy system (PHES). The proposed day-ahead EMS consists of two stages, namely the forecasting and the optimization stages. The forecasting module is responsible for predicting the wind power generation and load demand. A random forest (RF) method is used to perform the power forecasting after the extraction of the weather data features using a kernel principal component analysis (KPCA) technique. Then, a nonlinear programming (NLP)-based optimization technique is proposed to define the day-ahead optimal energy of the PHES. The purpose of the optimization is to maximize the profit cost in a day-ahead horizon, taking into consideration the system constraints.

Energy Management Strategy for Hybrid Energy Storage Electric Vehicles Based on Pontryagin’s Minimum Principle Considering Battery Degradation

The development of energy management strategy (EMS), which considers how power is distributed between the battery and ultracapacitor, can reduce the electric vehicle’s power consumption and slow down battery degradation. Therefore, the purpose of this paper is to develop an EMS for hybrid energy storage electric vehicles based on Pontryagin’s minimums principle (PMP) considering battery degradation. To verify the EMS, the hybrid energy storage electric vehicle model is first established. In the meantime, the battery cycle life trials are finished in order to develop a battery degradation model. Following that, a rule-based control approach and the PMP optimization algorithm are used to allocate power in a hybrid energy storage system (HESS) in a reasonable manner. Finally, a simulation experiment under urban dynamometer driving schedule (UDDS) settings verifies the established EMS, and the findings reveal that the suggested EMS has a lower energy consumption rate and battery deterioration rate than the rule-based method.

Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health

Due to the low efficiency and high pollution of conventional internal combustion engine vehicles, the fuel cell hybrid electric vehicles are expected to play a key role in the future of clean energy transportation attributed to the long driving range, short hydrogen refueling time and environmental advantages. The development of energy management strategies has an important impact on the economy and durability, but most strategies ignore the aging of fuel cells and the corresponding impact on hydrogen consumption. In this paper, a rule-based fuzzy control strategy is proposed based on the constructed data-driven online estimation model of fuel cell health. Then, a genetic algorithm is used to optimize this fuzzy controller, where the objective function is designed to consider both the economy and durability by combining the hydrogen consumption cost and the degradation cost characterized by the fuel cell health status. Considering that the rule-based strategy is more sensitive to operating conditions, this paper uses an artificial neural network for predictive control. The results are compared with those obtained from the genetic algorithm optimized fuzzy controller and are found to be very similar, where the prediction accuracy is assessed using MAPE, RMSE and 10-fold cross-validation. Experiments show that the developed strategy has a good generalization capability for variable driving cycles.

Distributed Deep Reinforcement Learning-Based Energy and Emission Management Strategy for Hybrid Electric Vehicles

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times.

Adaptive hierarchical energy management design for a novel hybrid powertrain of concrete truck mixers

Due to the characteristics of a significant difference between vehicle no-load and full load, time-varying double driving conditions, two-energy-source and two-energy-consumption-unit, and high coupling electromechanical-hydraulic system, this work studies two critical technologies, including powertrain design and energy management strategy development, to solve the tricky energy optimization problem of concrete truck mixers. A novel hybrid powertrain with a single-shaft parallel for the upper-part system and extended-range for the propulsion system is proposed. On that basis, an adaptive hierarchical energy management strategy is developed including two efforts. First, a rule-based hierarchical strategy with auxiliary power unit multi-mode switching for the upper and power splitting of system power units for the lower is designed. Second, a driving condition recognizer constructed by a hybrid-optimization-based random forest algorithm and a genetic algorithm optimization-based differentiated controller is designed, which can automatically switch to the optimal strategy under current driving conditions according to recognition results. Simulation results manifest that the designed optimized driving condition recognizer performance outperforms unoptimized by 2.50%. Compared with dynamic programming and the presented rule-based hierarchical strategy, the proposed adaptive hierarchical strategy with terminal SOC converging to the lowest level has better adaptability, economy, and real application performance under a real-world composite driving condition.

DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle

With decades’ development of energy management strategy in hybrid electric vehicle, learning-based method has been deemed as a key solution for energy economy and real time. However, current energy management strategy cannot reach an optimal energy economy performance and online update in a tolerable time lag. Aiming at solving these problems, an accelerated reinforcement learning method and an online-updated strategy are proposed in present work. Firstly, prioritized replay is applied in deep Q network with normalized advantage function for a fast convergence to an optimal strategy. Prioritized replay module endows weight to trained history data which is utilized in neural network training. The neural network is updated towards optimal strategy by weight in an effective way. Secondly, the online-updated strategy for fix-line hybrid electric vehicle is designed based on the accelerated reinforcement learning method and model predictive control. The predicted future road information generated by model predictive control in each time interval is delivered to the accelerated reinforcement learning module for online energy management strategy generating. Finally, with all efforts above, the online-updated strategy is carried out and validated through hardware-in-the-loop simulation. The results show that this approach promotes the energy economic performance while updating strategy in real time.

Self-Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

In order to achieve nearly optimal fuel economy for hybrid electric vehicles (HEVs) using an equivalent consumption minimum strategy (ECMS), it is necessary to dynamically tune the equivalent factor (EF). Unlike widely studied ECMSs that adapt EF to driving conditions, the proposed self-adaptive equivalent consumption minimization strategy (SECMS) applies a new idea of determining the EF from the historical driving conditions. To this end, a dynamic EF self-determining algorithm is designed according to the assumption that the electrical energy used at the current moment comes from past recuperation and charging, and an adaptive charge-sustaining algorithm is developed to balance the electrical self-sustainability and optimization performance. To show its effectiveness, the SECMS is implemented to a single-shaft parallel HEV over four standard driving cycles and benchmarked against two conventional ECMS-type control strategies: standard ECMS and adaptive ECMS (AECMS). The results show that the SECMS can achieve significant improvements regarding both the fuel economy and battery charge-sustainability compared to ECMS and AECMS. The proposed SECMS could facilitate a new development of energy management strategies for HEVs based on historical driving information.

Energy Management Strategy Development for Fuel Cell Hybrid Loaders

The application of fuel cell powered loaders will be an attractive option in the future. Compared with passenger cars and commercial vehicles, the operating conditions of loaders are complex and varying as the poor working environment, which pose a great challenge to the energy management problem of fuel cell hybrid loaders (FCHLs). This paper presents four energy management strategies (EMSs) based on dynamic programming, Pontryagin’s minimum principle, equivalent consumption minimization strategy, and model predictive control for FCHL comprising fuel cell and supercapacitors. The basic principles of the proposed approaches are described. Hydrogen consumption, fuel cell durability, and SoC maintenance were considered in the cost functions. Simulations are performed in the MATLAB environment under representative cycles of a wheel loader. Simulation results demonstrate the feasibility and effectiveness of the proposed EMSs.

Development of an on-line energy management strategy for hybrid electric vehicle

The Hybrid Electric Vehicle (HEV) seems to be one of the most promising short-term solution to improve the sustainability on the transportation sector. As well-known, the numerical analyses can give a substantial contribute during the preliminary vehicle design. In this context, the development of the Energy Management Strategy (EMS) represents the most challenging task. In this paper, an on-line local optimization EMS for a parallel/series hybrid vehicle is proposed to minimize the CO2 emissions. The proposed EMS, implemented in a dynamic simulation platform, is compared to the well-assessed off-line Pontryagin’s Minimum Principle (PMP). Firstly, the main differences regarding the energy management are highlighted in detail. Then, the EMSs are assessed in terms of CO2 emissions, putting into evidence that the proposed on-line strategy involves limited penalizations (3-4%) compared to the PMP target.

Battery state of charge management strategies for a real-time controller of a Plug-in Hybrid Electric Vehicle

This paper deals with the development of energy management strategies for a hybrid electric vehicle (HEV), aiming to reduce the global energy consumption. The vehicle is a Plug-in HEV, and its model had been validated on New European Driving Cycle (NEDC). A real-time model-based supervisory controller is implemented, called Equivalent Consumption Minimization Strategy (ECMS), and it is compared with the original heuristic control.Three ways to manage the energy stored in the battery along the driving mission are presented. Predictive information is then introduced to increase vehicle driveability. Conclusions summarize the benefits of such approach, showing satisfactory results also considering the driver comfort.

Simulation and Analysis of Energy Consumption for Plug-in Hybrid Electric Vehicles Based on Driving Cycles

The energy consumption of plug-in hybrid electric vehicles (PHEV) is influenced by driving cycles. In this paper, the influence of driving cycles has been analyzed for the PHEV energy consumption, which can provide a theoretical basis for the energy management strategy development. The K-means clustering method is used to divide driving cycles into different patterns. The energy consumption mathematical models of the PHEV powertrain are built based on the efficiency models of powertrain system. Then, the simulation models of energy consumption for PHEV are built based on MATLAB/Simulink software, and the simulation has been conducted using the CD-CS control strategy. Finally, the influence of driving cycles on energy consumption is obtained. The results show that taking full use of electrical energy is the more important to improve energy efficiency for PHEV, comparing with the traditional control of PHEV is only to enhance engine operating efficiency.

Energy management strategy development of a forklift with electric lifting device

In recent years, the forklift is facing two challenges energy saving and environmental. However, the hydraulic forklift has low transmission efficiency and energy efficiency. To solve the problem, this paper proposes an approach for the lifting hydraulic cylinder replaced by ball screw device.The lifting system is controlled directly with an electric motor drive instead of pump. First, we analyzed the working condition and energy flows of the forklift and proposed an energy recovery system for forklift. Second, we built the system model including supercapacitor model, vehicle model and the simulation model in AMESim. Due to the markedly changing loads, supercapacitor with high specific power and high durability seems the best choice for energy storage system. In addition the study of rule-based energy management control strategies on forklift with electric lifting device is discussed, which is validated and evaluated by simulation. The results show that the fuel consumption of the forklift with electric lifting device can be reduced by about 46.72% compared with the hydraulic forklift and its transmission efficiency is improved 82.3% when the loads is 3t. The energy saving effect of electric system is very significant.