Hybrid Electric Vehicle Control Research Articles

Adaptive control of plug-in hybrid electric vehicles based on energy management strategy and dynamic programming algorithm

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

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.

Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand

Abstract Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand.

Robust engine slipping start control of hybrid electric vehicles with uncertainty in clutch slipping torque and change in driver demand torque

The engine slipping start (ESS) benefits parallel hybrid electric vehicles from stable ignition and emission reduction. However, inappropriate coordination between the traction motor torque and clutch slipping torque during the ESS will lead to poor smoothness of the vehicle and failed start of the engine. Uncertainty in clutch slipping torque and change in driver demand torque bring tough challenges with sluggish convergence and intensive vehicle jerk in practice. To deal with this problem, a novel two-layer model reference adaptive controller (MRAC) which contains two parallel reference models is proposed to improve robustness and convergence rate simultaneously. On one hand, uncertainties of clutch slipping torque are divided into a low-frequency part and a high-frequency part, and adaptive laws based on the output feedback are designed contrapuntally to enhance robustness. On the other hand, two parallel reference models are designed to accelerate the tracking error convergence rate without changing the reference profiles, which is generated according to the driver demand torque in real time. To test the robustness and convergence rate, the proposed two-layer MRAC is compared with the classical MRAC and proportional–integral controller under the driving scenario with uncertain clutch slipping torque and abrupt change in driver demand torque. The sensitivity with different adaptive gains and low-frequency and high-frequency uncertainties in clutch slipping torque are examined. Finally, hardware-in-the-loop experiments are performed to verify the effectiveness of the proposed two-layer MRAC.

Design of a Modularization-Based Automation Performance Simulation Framework for Multi-Vehicle Interaction System

With the electrification and connectivity of vehicles in transportation, traditional vehicles with single drivetrains are being replaced by pure electric or hybrid electric vehicles (HEVs). This evolution has given rise to diverse electromechanical coupling drivetrains. There is a pressing need to update simulation software to assess the economic performance of vehicles in various environments, and promote sustainable development and energy conservation. This paper presents a unified framework for the construction and automated operation of large-scale automated vehicle simulations with multiple drivetrain types, facilitating synchronous information exchange among vehicles. Central to the framework is the development of a plug-and-play vehicle model based on a modular component design, facilitating the rapid assembly of vehicles with varied drivetrain configurations and standardizing simulation file management. Additionally, a standardized simulation process construction is established to accommodate the automated operation of simulations. Furthermore, a data scheduling method among vehicles is introduced to achieve multi-vehicle interconnection simulation. Finally, the effectiveness of the framework is demonstrated through a case study involving queue-following control for HEVs. This framework aims to provide a comprehensive solution for quickly establishing automated simulation environments for multi-vehicle interaction, enhancing model reusability and scalability.

Deep Reinforcement Learning-Based Integrated Control of Hybrid Electric Vehicles Driven by Lane-Level High-Definition Map

For automobiles, the innovation of autopilot, connected communication, and new energy have been treated as the trend. This paper proposed a deep reinforcement learning-based integrated control strategy driven by the lane-level high-definition map for hybrid electric vehicles. Firstly, an optimal route with a length of 1277km from Shanghai to Beijing was determined from Google Maps. Based on the longitude, latitude, and elevation data from Google Earth, a lane-level driving environment was built and the global speed was preplanned based on the limited and safe speed. Secondly, a multi-policy integrated control framework and training method were proposed, adopting two types of deep reinforcement learning algorithms to control the velocity and steering angle of the vehicle layer and power distribution of the powertrain. Finally, processor-in-the-loop tests were completed by adopting NVIDIA Jetson Nano and Jetson AGX Xavier. Results show that the integrated control strategy achieves stable control effects on velocity and steering angle based on the environment model, gets a fuel economy of 6.77L/100km, and presents ideal efficiency on embedded devices. Moreover, the results are compared with three other typical powertrains in long-distance driving, and the comparison of driving costs and infrastructure emphasizes the necessity of the development of hybrid electric vehicles.

Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene

To address the comprehensive optimization problem of driving performance and fuel economy in the driving process of hybrid electric vehicles (HEV) in the car-following scene in the connected environment, an energy management strategy (EMS) based on front vehicle speed prediction and ego vehicle speed planning is designed by combining intelligent transportation system (ITS) technology. The front vehicle speed predictor is first established based on the long short-term memory neural network (LSTM). Then, based on the predicted speed of the front car, the predictive cruise control (PCC) strategy is designed for realizing the speed control in the car-following scene by combining it with the adaptive cruise control (ACC). Finally, based on the planned vehicle speed, deep reinforcement learning (DRL)-based EMS is used to optimize the power distribution among different power components of HEVs. The analysis of simulation results under the SUMO-Python joint simulation platform verifies the proposed strategy.

Data-driven energy-efficient speed planning and adaptive car-following control for commuter plug-in hybrid electric vehicles

This paper proposes a hierarchical adaptive eco-driving control scheme for commuter plug-in hybrid electric vehicles (PHEVs), which contains the data-driven speed planner in the upper layer and the speed tracking controller in the lower layer. Considering that the speed planning for PHEVs with the goals of fuel economy and comfort involving both coupled motion and powertrain dynamics can result in an enormous computational burden, a data-driven speed planner is designed based on traffic information of the commuter PHEV, where the energy consumption and battery state are described through two models by the neural networks, respectively. As such, the suggested speed ensuring fuel economy and comfort for the vehicle driving on this route can be obtained with an acceptable computational burden. Then, facing the immeasurable acceleration of the preceding vehicle and slow time-varying road grade, an adaptive car-following speed controller is designed through the backstepping recursive technique to track the suggested speed accurately and safely. The control effects can be adjusted through vehicle-to-cloud (V2C) communication. The simulation results demonstrate the effectiveness and advantages of the proposed scheme in improving fuel economy.

Research on Mode Switching and Energy Management of Hybrid Electric Vehicle

Aiming at the optimization of mode switching control and energy management strategy for parallel hybrid electric vehicles, a mode switching control method and A-ECMS steady-state energy management strategy is proposed. Firstly, in order to effectively suppress the impact caused by mode switching, and a fuzzy controller is designed for clutch engagement displacement. Then, on the basis of ensuring the smoothness of mode switching, A-ECMS control strategy based on SOC feedback is established. Finally, Isight and Cruise are used to build a co-simulation platform, and multi-island genetic algorithm is used to study the influence of initial value selection of equivalent factor on the controller. The results show that the proposed mode switching control and energy management control strategy, combined with A-ECMS strategy, can effectively improve the vehicle fuel economy on the basis of ensuring the mode switching smoothness.

Sliding mode coordinated control of hybrid electric vehicle via finite-time control technique

During the transient mode switching process of the hybrid electric vehicle (HEV) from motor driving mode to hybrid driving mode, dynamic coordinated control of different components is essential to improve the vehicle comfort and dynamic performance. The key to highly quality mode switching control includes fast and stable speed and/or displacement tracking of the engine and motor. The transient mode switching stages of the HEV is divided in this paper. On this basis, by combing the nonlinear sliding mode control and the finite-time stability theory, the global fast integral terminal sliding mode controller (GFITSMC) is designed for the transition stages involving clutch slip. The GFITSMC consists of the global fast integral terminal sliding mode surface (GFITSMS) and the non-smooth reaching law (NSRL). In order to improve the controller convergence and anti-disturbance performance, the proposed controller is synthesized from the perspective of finite-time stability. It is proved that, with proper NSRL and GFITSMS parameters, the speed and displacement tracking error of the motor and engine can reach the sliding mode surface and further converge to zero in a finite time. Simulation and hardware-in-the-loop (HIL) tests are performed to validate the effectiveness of the proposed control method. Research results demonstrate that the proposed strategy not only achieves faster transient mode switching by improving the state trajectory tracking performance, but also reduces the longitudinal jerk caused by the transient mode switching significantly.

Energy Management Based on Mixed-Integer Nonlinear Model Predictive Control for Hybrid Electric Vehicles

Energy Management Based on Mixed-Integer Nonlinear Model Predictive Control for Hybrid Electric Vehicles

Design and Control of Hybrid Electric Light Rail Vehicles With Open Ended Winding Machines

Design and Control of Hybrid Electric Light Rail Vehicles With Open Ended Winding Machines

Model predictive control for multimode power-split hybrid electric vehicles: Parametric internal model with integrated mode switch and variable meshing losses

Model predictive control (MPC) is one of the most promising energy management strategies for hybrid electric vehicles. However, owing to constructive complexity, the multimode power-split powertrain requires dedicated mathematical tools to model the mode switch and transmission power losses within the internal model of the controller. Thus, the transmission losses are usually neglected and the mode switch is optimised through offline simulations. This paper proposes an MPC internal model relying on a parametric approach available in the literature, which provides a unique formulation for modelling any power-split transmission and assesses the transmission meshing losses. The objectives, which cover a gap in the literature, are: 1) to integrate the discrete problem of the mode switch in a continuous formulation of the internal model; 2) to compare MPC internal models with different complexity, and evaluate how the consideration of meshing losses and efficiency of the electric machines affect the controller performance. The results on a case study vehicle, i.e., the Chevrolet Volt, suggest that a simplified internal model deteriorates the fuel consumption performance by less than 2 %, while the integrated mode switch is comparable to the offline strategy.

Predictive cruise control for hybrid electric vehicles based on hierarchical convex optimization

Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%.

Dynamic Switching Control of Power-Split Hybrid Electric Vehicles Based on Time Delay Prediction and Interference Compensation

The deterioration of vehicle dynamics caused by the simultaneous action of time delay and interference is a key issue for the switching control system in a power-split hybrid electric vehicle (PS-HEV). This paper presents an innovative compound control method based on extended state observer (ESO) incorporating time delay during the mode transition process. First, the coupling effect of time-varying delay and external disturbance during the typical mode switching process is analyzed in depth, and the double deterioration mechanism of the two coupling effects on vehicle dynamics and ride comfort is revealed. To accurately estimate the system disturbance under the influence of time delay, an unconventional ESO is designed further by adopting the Markov chain time delay prediction model, and the main tool for interference observation is selected by comparing the practicability, reachable bandwidth and observation accuracy. Moreover, the stable mode switching performance and interference suppression are thoroughly investigated, and the smooth transmission of multi-power is realized through the multi-module fusion design of the compound coordinated controller. Finally, the hardware-in-the-loop test is implemented to validate the effectiveness of the control strategy, which also provides a novel idea and lays a theoretical foundation for the optimal design of the PS-HEV controller.

Integrated Optimization of Power Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Integrated Optimization of Power Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Robust adaptive control for hybrid electric vehicles using sliding mode control

The complicated transient coupling driving performance of the electromechanical coupling driving system for hybrid electric vehicles can be guaranteed by reasonably distributing the power between the engine and the electric machine. However, the disturbances resulting from system parameter perturbation, model uncertainty, unknown road condition, and uncertain preceding vehicle acceleration would degrade the control performance. To this issue, this article investigates a robust adaptive control scheme based on sliding mode control techniques to handle the complicated transient coupling driving problem for the electromechanical coupling driving process. In the robust adaptive control method, the coordinated control mechanism is designed using the electric machine to compensate for the response deviation of engine torque. Meanwhile, the double-loop adaptive terminal sliding mode speed-tracking control scheme and the adaptive dynamic sliding mode torque tracking control method are designed for the engine and the electric machine, respectively. The proposed control scheme ensures accurate tracking of the desired trajectories of the engine and electric machine in the presence of parameter perturbation and the unknown driving condition. Both simulation and actual vehicle experimental results demonstrate the effectiveness and practicality of the proposed control strategy.

Hierarchical eco-driving control for plug-in hybrid electric vehicles under multiple signalized intersection scenarios

With the development of vehicle-to-everything (V2X) and autonomous driving technologies, plug-in hybrid electric vehicles (PHEVs) enable to absorb surrounding information to enhance economic driving. This study proposes a dynamic inverse hierarchical optimization method, which incorporates traffic-signal phase and timing as well as road data, to plan economic velocity and improve PHEV energy consumption. The hierarchical control framework determines the desired speed and arrival time in the upper layer using the shortest path faster algorithm. The lower level accounts for multi-objective velocity planning based on particle swarm optimization and Pareto theory. The inverse layering method solves the economic velocity optimization problem. With the support of V2X and autonomous driving technologies, the method enhances energy efficiency and computational efficiency in PHEVs through dynamic inverse hierarchical optimization. Simulation results highlight that the proposed algorithm leads to 7.43% improvement in energy consumption economy and the reduced calculation time, compared to the existing solutions. The hardware-in-the-loop experiments validate the real-time applicability of the proposed algorithm.

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.