Control Strategy For Hybrid Electric Vehicles Research Articles

Optimised fuzzy control strategy applied to parallel hybrid electric vehicle

As the Hybrid Electric Vehicle (HEV) includes at least two energy sources, appropriate control of these energy components is necessary to maximise the efficiency of the HEV, which is the purpose of the HEV control strategy. This paper describes a Particle Swarm Optimization (PSO)-Fuzzy control approach applied to HEV with parallel configuration. This approach employs a fuzzy controller, wherein the parameters of the Membership Functions (MFs) are fine-tuned using the PSO algorithm. The primary goal is to reduce energy consumption with consideration of battery State of Charge (SoC) maintenance. The effectiveness of this strategy will be proven by simulations under a variety of driving cycles.

New generation of passenger vehicles: FCV or HEV?

This paper compares the performance and parameter characteristics of Fuel Cell Vehicles (FCV) and Hybrid Electric Vehicles (HEV) with a view towards an objective assessment of the relative performance of these vehicles. Firstly, the main characteristics of hybrid electric vehicles (HEVs) as low emission vehicles (LEVs), including presumed high efficiency is considered. Then, comparisons for well-to-wheels emissions for various vehicles are presented. Well-to-wheels efficiencies, emissions, and fuel economy are also compared for FCVs and HEVs. In addition, other issues like battery types for HEVs and HFCVs are explored in this paper. The potential control strategies for FCVs and HEVs will be discussed and compared. In both FCVs and HEVs, best control strategies need to rely on predicting the driver command, which presents a particularly challenging opportunity for further development. Finally, this paper gives the comparison of total costs for FCVs and HEVs.

Control strategy optimization of hybrid electric vehicle for fuel saving based on energy flow experiment and simulation

Energy flow analysis is one of the most effective methods for assessing and improving vehicle efficiency. It can provide the most effective support for the development and optimization of a hybrid electric vehicle (HEV) control strategy for improving the performance and energy saving of HEVs. In this study, an energy flow experiment of a series–parallel HEV is conducted in a climate chamber under the Worldwide Light-duty Test Cycle to address high fuel consumption. Then, GT-Suite and MATLAB/Simulink co-simulation models for the tested vehicle are built and calibrated. The problems of the original HEV control strategy are addressed using the analysis of vehicle energy flow distributions, operating conditions, and energy efficiency of key parts. Then, two optimized vehicle control strategies (termed Opt1 and Opt2) are employed to test vehicle fuel savings using a calibrated co-simulation model. The specific optimized vehicle control strategies primarily include canceling the series and parallel charging in the braking state, adding the engine direct-drive mode, and reducing the proportion of the parallel mode. The simulated results show that the 100 km fuel consumptions of Opt1 and Opt2 are reduced by 7.1% and 7.5%, respectively, compared with the original vehicle. The proportion of energy that drives the vehicle directly through the engines of Opt1 and Opt2 is increased from 45.7% to 70.2% and 61.6%, respectively. The total vehicle energy losses for Opt1 and Opt2 decrease from 29.0% to 22.5% and 22.9%, respectively. Although the engine of the original vehicle exhibits high thermal efficiency, the efficiency of the entire powertrain system is the lowest, resulting in the highest fuel consumption.

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

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

Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-in Hybrid Electric Vehicle

In the current scenario, where environmental concern determines the evolution of passenger cars, hybrid electric vehicles (HEV) represent a hub in the automotive sector to reach net-zero CO2 emissions. To fully exploit the energy conversion potential of advanced powertrains, proper energy management strategies are mandatory. In this work, a simulation study is presented, aiming at developing a new control strategy for a P3 parallel plug-in HEV (PHEV). The simulation model is built on MATLAB/Simulink. The proposed strategy is based on an alternative utilization of the thermal engine and electric motor to provide the vehicle power demand (efficient thermal/electric skipping strategy (ETESS)). An adaptive function is then introduced to develop a charge-blended control strategy. Fuel consumption along different driving cycles is evaluated by applying the novel adaptive-ETESS (A-ETESS). To have a proper comparison, the same adaptive function is built on the equivalent consumption minimization strategy (ECMS). Processor-in-the-loop (PIL) simulations are performed to benchmark the A-ETESS. Simulation results highlighted that the proposed strategy provides for a fuel economy similar to ECMS (worse of about 2.5% on average) and a computational effort reduced by 99% on average, opening the possibility of real-time on-vehicle applications.

Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set

For off-road hybrid electric vehicles (HEV), due to the variability of road environments and the dynamic and deferred response characteristic of the engine generator set (EGS) for off-road HEVs, it is difficult to coordinate the power output of multi-energy sources to meet the demand power of the vehicle. Therefore, designing an efficient power control strategy for off-road HEVs remains a major challenge. Motivated by this issue, an online learning predictive power coordinated control strategy for off-road HEVs is proposed in this study. Firstly, the online sequential extreme learning machine is used for short-term power prediction for the first time. With the online learning capability, the precision of power prediction under irregular road conditions is significantly improved. Secondly, to determine the optimal control behavior of power distribution between two energy sources, a novel predictive adaptive equivalent consumption minimization strategy is designed. The equivalent factor is rolling optimized in the prediction horizon to maintain battery state of charge and ensure fuel economy. Thirdly, considering the actual response process of EGS, a one-step-ahead coordinated control is presented to guarantee adequate electric power output. Finally, the performance of the proposed strategy is verified by simulation and hardware-in-loop test. The results show that fuel consumption using the proposed strategy is reduced by 6.72% and 8.63% over benchmark method under the two test driving cycles, respectively. Meanwhile, the setting time of EGS power is decreased by 61.12% and 64.63% to ensure the dynamic performance of vehicle.

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

Well-designed energy management strategies are essential for the good operation of Hybrid Electric Vehicles (HEVs) in terms of fuel economy and pollutant emissions reduction, regardless of the specific powertrain architecture. The goal of this paper is to propose two innovative supervisory control strategies for HEVs derived from different optimization algorithms and to assess HEVs’ fuel consumption reduction (compared to conventional vehicles). These approaches are derived from the literature and modified by the authors to present novel algorithms for the optimization problem. One is based on Dynamic Programming (DP), here referred to as the Forward Approach to Dynamic Programming (FADP) and introduces a different implementation of the DP to achieve computational and accuracy benefits. The other is based on the Equivalent Consumption Minimization Strategy (ECMS) approach, and it adapts to the latest driving conditions using information gathered in a finite-length backward-looking horizon. These techniques are used to achieve the optimal power share between the thermal engine and the battery of a parallel HEV. Their performances are compared and analysed in terms of achieved fuel economy and computational time with respect to conventional DP and Pontryagin’s Minimum Principle (PMP) approaches.

High‐Voltage Topological Architecture‐Based Energy Management Strategy of the Plug‐In Hybrid Powertrain System

Hybrid technology (including plug‐in hybrid) integrates the advantages of traditional automobile technology and pure electric technology, which can greatly reduce fuel consumption and improve emissions. It has become one of the main technologies developed at present and in the next 15∼20 years. Energy management is the core algorithm of hybrid electric vehicle control strategy, and it is the focus of current research. However, these studies mainly focus on the high efficiency of control assembly, optimal management of power system energy, and maximum recovery of renewable energy but have not considered energy distribution management and optimization between the power battery and the low‐voltage battery. Hence, based on the high‐voltage topology of the plug‐in hybrid system, this paper proposes the optimal energy management strategy between the power battery and the low‐voltage battery under different working conditions. The charging and discharging characteristics of the power battery under different electric quantities are also combined. The experimental results show that based on the optimized energy management strategy, the pure electric driving range is increased by 6% under NEDC condition for a C‐class plug‐in hybrid car, and the energy‐saving effect of the vehicle is further improved.

Neural Network- and Fuzzy Control-Based Energy Optimization for the Switching in Parallel Hybrid Two-Wheeler

Optimization of a two-wheeler hybrid electric vehicle (HEV) is a typical challenge compared to that for four-wheeler HEVs. Some of the challenges which are particular to two-wheeler HEVs are throttle integration, smooth switching between power sources, add-on weight compensation, efficiency improvisation in traffic, and energy optimization. Two power sources need to be synchronized skillfully for optimum energy utilization. A prominent variant of HEV is that it easily converts conventional scooters into parallel hybrids by “Through-the-Road (TTR)” architecture. This paper focuses on three switching control strategies of HEVs based on the state of charge, fuzzy logic, and neural network. Further, to optimize energy usage, all these control strategies are compared. Energy management control for the TTR model is developed with vehicle parameters in the Simulink environment and simulated using the “World Harmonized Motorcycle Test Cycle” (WMTC) drive cycle. The multivariable input model is presented with a fuzzy rule-based hybrid switching control. A similar system is also modeled with a neural network-based decision control and the observations are tabulated for the fuel economy and energy management. Simulation results show that the neural network-based optimization results in minimal energy consumption among all three hybrid operations.

Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle, but also effectively save fuel and reduce emissions. In this paper, the construction of model predictive control in hybrid ele... | Find, read and cite all the research you need on Tech Science Press

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

The development and numerical verification of a compromised real time optimal control algorithm for hybrid electric vehicle

Hybrid electric vehicles (HEV) have been recognized as a viable technology to significantly improve the fuel economy of on-road vehicles operated in urban areas featuring frequent stop-and-go operation. The optimization of the HEV control strategy is a dynamic optimal problem involving a nonlinear cost function and several nonlinear constraints. To take advantage of the global optimality of dynamic programming (DP) and the real-time capability of the equivalent consumption minimization strategy (ECMS) in optimizing the HEV operation strategy, a model that combines ECMS with DP is proposed in this paper, which is defined as ECMSwDP. Using this approach, the optimal equivalent factor λ for ECMS is derived using DP under various driving conditions and the different grades of synthetic driving cycle. The fuel economy simulated using the ECMSwDP control strategy is comparable to the optimal solution derived using DP. The potential of the real-time control strategy developed using ECMSwDP to improve fuel economy is demonstrated using a transit hybrid electric bus as an example.

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.

Whole-Day Driving Prediction Control Strategy: Analysis on Real-World Drive Cycles

The Whole-Day Driving Prediction (WDDP) concept is a unique plug-in hybrid electric vehicle (PHEV) control strategy that uses the day’s planned travels to determine if a small range-extending engine should be turned on at the start of each day. This control strategy allows for the use of a very small engine, in contrast to commercially available PHEVs, which generally have engines large enough to propel the vehicle. This paper presents modeling and simulation results for WDDP vehicles on the real-world logged driving cycles of 100 drivers who were each logged for between 2 and 6 weeks. The simulation results show that with an engine size between 5 and 7 kW, the WDDP vehicle with a 35-kWh battery has similar range capabilities to a pure EV with a 60-kWh battery. The consequence is that purchase price can be decreased while keeping similar range performance, encouraging a higher market penetration of plug-in vehicles. The results of this paper show that the WDDP concept is viable for real-world use, and has the potential to reduce plug-in vehicle costs while achieving driving ranges similar to the new long-range EVs recently introduced to the market.

一种动态规划算法模型预测控制在混合动力汽车控制策略中的应用

一种动态规划算法模型预测控制在混合动力汽车控制策略中的应用

Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm

Hybrid electric vehicle (HEV) control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a “memetic algorithm (MA)” to optimize the control parameters in parallel HEVs. The “local search” mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR) and is set up according to an electric assist control strategy (EACS) to minimize the fuel consumption (FC) and emissions (HC, CO, and NOx) of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC), Federal Test Procedure (FTP), Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC), and urban dynamometer driving schedule (UDDS) are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.

An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning

In order to realize the online learning of a hybrid electric vehicle (HEV) control strategy, a fuzzy Q-learning (FQL) method is proposed in this paper. FQL control strategies consists of two parts: The optimal action-value function Q*(x,u) estimator network (QEN) and the fuzzy parameters tuning (FPT). A back propagation (BP) neural network is applied to estimate Q*(x,u) as QEN. For the fuzzy controller, we choose a Sugeno-type fuzzy inference system (FIS) and the parameters of the FIS are tuned online based on Q*(x,u). The action exploration modifier (AEM) is introduced to guarantee all actions are tried. The main advantage of a FQL control strategy is that it does not rely on prior information related to future driving conditions and can self-tune the parameters of the fuzzy controller online. The FQL control strategy has been applied to a HEV and simulation tests have been done. Simulation results indicate that the parameters of the fuzzy controller are tuned online and that a FQL control strategy achieves good performance in fuel economy.

A Dynamic Programming-Based Control Strategy with Optimum Efficiency of Hybrid Energy Storage System for HEV

A reasonable and effective control strategy for HEV (Hybrid Electric Vehicle) with HESS (Hybrid Energy Storage System) can improve the system efficiency and battery service life. A dynamic programming-based global optimal control strategy which fully considered the efficiency of each component in HEV is presented. To compare with the nonlinear proportion factor dynamic coordination strategy, the fuel consumption is not increased. The cycle numbers of battery are further decreased which benefits its cycle life improvement. The regenerative energy callback ratio improves 3% under the shanghai drive cycle as an example. Since this optimization algorithm considers efficiency of each subsystem, the efficiency optimum under the selected driving cycle was realized. It can also provide a reference for the non-linear scaling factor allocation strategies to determine the scale factors.

Hybrid Base and Parallel Hybrid Electric Vehicle Control Strategy

Automotive hybrid technology development and mechanical, electrical,engine,energy technology is closely related with the development of these areas of technology, the development of hybrid technology also will be extensive. How to optimize the control strategy is the key to achieve hybrid cars low fuel consumption, low emissions targets . In the automotive power and meet other basic technical performance,and cost requirements of the premise,characteristics and operating conditions for the car's various components , control strategies to achieve a reasonable distribution of energy in the motor between the engine and effective,so that the vehicle the highest system efficiency,access to the largest vehicle fuel economy,low emissions and smooth driving performance.