Hybrid Electric Vehicle Control Research Articles

An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles

This study aims to develop a hybrid energy storage system (HESS), targeting a commercialised Hybrid Electric Vehicle model (Hyundai Sonata), that consists of battery and supercapacitor cells, to evaluate its benefits on the battery's health and vehicle's performance. The different possible configurations of combining the two energy storage devices are discussed, while a semi-active configuration is considered for the purpose of simulation and testing, where the supercapacitor is connected to a bidirectional DC-DC converter, implemented with a newly developed control logic for the HESS. The analysis of the HESS is done by modelling the vehicle powertrain on the simulation software Ricardo IGNITE, while adapting the WLTC Class 2 drive cycle. The results are validated by comparing HESS model to the sole battery model, which showed the working status of the battery is stabilised by the addition of the supercapacitor in the HESS model during both the traction and regenerative modes. There is a significant reduction in the battery peak current, 15.26% and 20.54% for the charge and discharge current, respectively. Other improvements include a 0.43% increase on the average battery SOC, a 6.8% decrease in the battery's maximum temperature and 47.56% decrease in the average battery power. On the other hand, there was a 0.46% increase on vehicle's average L/100 km fuel consumption, due to losses in the supercapacitor system. Various other results are also compared in the discussion section, justifying the addition of the supercapacitor. The investigation finishes off by conducting a parametric study based on the number of supercapacitor modules in parallel, and comparing results such as current, power, fuel consumption and battery SOC. The results show that 2 supercapacitor modules provided the optimal results in terms of battery's current and power behaviour.

Coordinated control algorithm of lateral stability of hybrid electric commercial vehicle

Focusing on the problem of mutual interference between the vehicle controller and the lateral stability control system in the stability control of hybrid electric vehicles (HEV), this study proposes a coordinated control algorithm for hybrid power systems and lateral stability, which can meet the real-time control requirements of the vehicle stability. First, the overall architecture of coordinated control between the hybrid power system and the vehicle lateral stability control system is designed. The judgment logic of lateral stability control system intervention and exit is determined on this basis. Second, in the upper layer of the control algorithm, the model predictive control algorithm is used to calculate the additional yaw moment required to maintain stability control under lateral instability of the vehicle with the vehicle two-degrees-of-freedom model as a reference. The motor output torque control and braking force distribution are then carried out in the lower layer of the control algorithm. The target braking wheel is determined according to the oversteering and understeering state of the hybrid vehicle. The additional yaw torque distribution control is formulated based on the quadratic programing algorithm. The strategy is to perform dynamic compensation for unstable or critically unstable vehicles to solve the optimal control problem under multiple constraints. Finally, through Matlab/Simulink and Trucksim co-simulation and hardware-in-the-loop test, the validity and real-time performance of the control algorithm proposed are verified to solve the interference problem in the lateral stability control process of HEVs and further improve the stability of vehicle rideability and active safety.

A Batteryless Series Hybrid Electric Vehicle Control

Energy management/control in hybrid electric vehicles (HEVs) is of a prime for many electric vehicle manufacturers and researchers. In our previous work, a control based on the singular perturbation (SP) approach was designed and applied to a series HEV for managing/controlling its power. This power supply, an internal combustion engine (ICE) that was mechanically connected to an electric motor (EM), which is a permanent magnet synchronous motor (PMSM), through a flywheel. However, the energy losses, which were not taken into account in the system model and the control function design, caused a significant current control error. In this article, we propose improving the control system performance using an observer that estimates the energy losses in a closed loop system with the time delay. Stability of the closed loop system with the observer is analyzed using a SP method. The proposed control strategy that uses the observer of the energy losses is compared to the control approach of our previous work and its efficacy is demonstrated experimentally on a reduced power scale series HEV.

Barrier function-based adaptive terminal sliding mode control of plug-in hybrid electric vehicle with saturated control actions

Plugin hybrid electric vehicles in contrast to traditional hybrid electric vehicles (HEV) are getting enormous attention in the modern electric transportation market as they address the environmental issues, fuel economy crisis, and CO2 emission. Fuel cell-based PHEV with smart charging mechanism is an important application of the clean energy generation and storage system. There are two main subsystems in a PHEV: A hybrid energy storage system (HESS) and an integrated charging unit (ICU). In this study, an effective control strategy for the charger and HESS comprising three power sources: Fuel cell (FC), battery, and a superconducting magnetic energy storage (SMES) connected to the DC bus is presented. Due to the non-linearity of power conditioning circuits in ICU and HESS, designing a robust nonlinear controller is still a challenge. A barrier function-based adaptive terminal sliding mode controller is proposed to ensure the finite-time convergence of the output variable. It also maintains it in a predefined neighborhood of zero without overestimating the control gain and hence exhibits better dynamic performance in the presence of external disturbances of unknown upper bound. The asymptotic stability of the system has been ensured by using the Lyapunov stability criterion. Finally, the energy management algorithm in HESS using the state of charge of the battery and SMES as the decisive factor is incorporated to maintain the stability of the system under varying load conditions. The performance of the proposed controller has been compared with conventional nonlinear control techniques like adaptive terminal sliding mode control (ATSMC) and supertwisting sliding mode control (STSMC) by using MATLAB/Simulink. The performance of the system is further validated by using a C2000 Delfino F28379D MCU dual-core microcontroller with a hardware-in-the-loop (HIL) setup.

Robust adaptive nonlinear control of plugin hybrid electric vehicles for vehicle to grid and grid to vehicle power flow with hybrid energy storage system

Plugin hybrid electric vehicles (PHEVs) can solve the concerns of toxic gases emissions from fossil fuel. The PHEV under consideration consists of an on-board smart charger and a hybrid energy storage system (HESS) composed of the battery as a primary power source and an ultracapacitor (UC) as a secondary power source conjoined with two DC–DC bidirectional buck-boost converters. The on-board charging unit comprises of an AC–DC boost rectifier and DC–DC buck converter. The entire system’s state model has been derived. An adaptive supertwisting sliding mode controller (AST-SMC) has been proposed for the unitary power factor correction at the grid side, tight voltage regulation of the charger and the DC bus, adaptation of time varying parameters and tracking of the currents involving the variation in the load profile. A genetic algorithm has been applied for optimizing the cost function of the controller gains. Key results refers reduction of chattering, adaptation of parametric variations, nonlinearities and external disturbances of the dynamical system. HESS results presents negligible convergence time and overshoots/undershoots even at the transients, and no steady state error. For the driving mode, the switching between dynamic and static behaviors and for parking mode, vehicle to grid (V2G) and grid to vehicle (G2V) operations have been proposed. In order to make nonlinear controller intelligent to achieve the V2G and G2V functionality, a state of charge based high-level controller has also been proposed. A standard Lyapunov stability criteria has been used to ensure asymptotic stability of the entire system. The proposed controller has been compared with sliding mode control (SMC) and finite time synergetic control (FTSC) by the simulation results using MATLAB/Simulink. Also, the hardware in loop setup has been used to validate the performance in real-time.

Solution Set Augmentation for Knee Identification in Multiobjective Decision Analysis.

In multiobjective decision making, most knee identification algorithms implicitly assume that the given solutions are well distributed and can provide sufficient information for identifying knee solutions. However, this assumption may fail to hold when the number of objectives is large or when the shape of the Pareto front is complex. To address the above issues, we propose a knee-oriented solution augmentation (KSA) framework that converts the Pareto front into a multimodal auxiliary function whose basins correspond to the knee regions of the Pareto front. The auxiliary function is then approximated using a surrogate and its basins are identified by a peak detection method. Additional solutions are then generated in the detected basins in the objective space and mapped to the decision space with the help of an inverse model. These solutions are evaluated by the original objective functions and added to the given solution set. To assess the quality of the augmented solution set, a measurement is proposed for the verification of knee solutions when the true Pareto front is unknown. The effectiveness of KSA is verified on widely used benchmark problems and successfully applied to a hybrid electric vehicle controller design problem.

Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate Dynamic Programming

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. Additionally, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.

Optimal Energy Management of Series Hybrid Electric Vehicles With Engine Start–Stop System

This paper develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (HPTS), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process, while also targeting charge sustaining operation. The performance of HPTS is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the HPTS and also indicate its potential to be used as the benchmark strategy for high fidelity HEV models, where DP is no longer applicable due to computational complexity.

Co-Optimization of Design and Control of Energy Efficient Hybrid Electric Vehicles Using Coordination Schemes

AbstractDesign and control co-optimization studies for hybrid vehicles have been proposed in the past. However, such works suffer from difficulties arising due to (a) diverse real- and integer-valued variables, (b) complex nonlinear powertrain dynamics and design interconnections, (c) conflicting objective functions with path constraints, and (d) high computational resources requirements. To meet these challenges, this study presents an efficient co-optimization framework for hybrid electric vehicles (HEVs) which is built using existing algorithms and coordination schemes. Particular emphasis is given to the simultaneous scheme and the decomposition-based scheme. The decomposition-based scheme with the problem decomposition proposed in this work can efficiently handle multitime scale state variables and both integer- and real-valued design and control optimization variables. This is demonstrated by solving the mixed-integer optimal design and control problem of a series hybrid vehicle over a 1-h long drive cycle with time discretization of 1 s. The problem complexity is elevated by using an increasing number of state variables (including battery state of charge, battery energy, and after-treatment system temperature), control variables (such as the engine power and engine on/off), and design parameters (such as the number of battery cells and the type and size of the engine). In addition, a multi-objective cost function is used to find a tradeoff solution between fuel consumption and emissions minimization. The results show that in terms of optimality of the solution, the decomposition-based scheme is comparable with the simultaneous but can give a 14% improvement in computational performance. The effectiveness of the proposed framework is demonstrated by comparing the co-optimization results against a baseline case in which only the optimal control problem is solved. The co-optimized solution yields up to 3.7% average genset efficiency improvement and a fuel consumption reduction to 1.6 kg from 2.5 kg, which is further reduced to 1.5 kg by adding the engine on-off control. Finally, a decision matrix is developed to provide guidance on the selection of the optimization algorithm and coordination scheme for any problem at hand.

Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation.

Multi-physical cooperative control of plug-in hybrid electric vehicles via cyber hierarchy and interactional network

Cyber hierarchy and interactional network (CHAIN) concept control of autonomous plug-in hybrid electric vehicles is presented to fully release the potential of the vehicle components while guaranteeing the driving safety and stability, which was divided into cyber intelligent driving system (cyber-IDS) on the cyber-end and vehicle intelligent driving system (vehicle-IDS) on the vehicle-end. On the cyber-end, the state information of surrounding vehicles and infrastructures about traffic conditions are uploaded via vehicle-to-everything (V2X) and processed in the cloud to obtain future driving behavior via car following theory, achieving automatic following control. On the vehicle-end, the vehicle-IDS determines the set of its future control variables, and simultaneously provides an optimized control sequence in the prediction horizon by performing approximate global optimization instead of local optimization. The system global optimization needs the high coupling between the macro physical process(cyber-IDS) and the micro physical process(vehicle-IDS), and only the macro planning cannot be directly applied to the micro process control. The nonlinear system at the vehicle-end is decomposed into linear systems around each working point, and the dynamic characteristics between nodes are connected by neural network based on radial basis function of heterogeneous distribution coefficients. By introducing the genetic algorithm for time series, the CHAIN autonomous driving strategy capable of macro and micro coupling can realize the accurate optimal control under the actual driving conditions, which not only eliminates random fluctuations, but also enables a full-lifespan fuel saving of autonomous hybrid vehicles as well as no damage to battery health.

Design of downhill regenerative braking control method for hybrid electric vehicle

In order to improve the downhill regenerative braking control effect of hybrid electric vehicle (HEV), this paper designs a downhill regenerative braking control method for hybrid electric vehicle (HEV). Firstly, the structures of regenerative braking system, auxiliary braking system and eddy current retarder (ECR) are analyzed. Secondly, the braking torque of ECR when HEV downhill braking is calculated. Then, the braking process of HEV is dynamically analyzed, and the braking force distribution constraint of HEV is realized according to the relevant laws and regulations. Finally, the downhill regenerative braking control of HEV is realized based on the braking force distribution coefficient. The results show that when the vehicle speed is up to 120 km·h, the speed control can still be completed within 2 s by using the proposed method, indicating a good regenerative braking effect.

Configurations and Control Strategies of Hybrid Powertrain Systems

The configuration and control strategy of hybrid powertrain systems are significant for the development of hybrid electric vehicles (HEV) because they significantly affect their comprehensive performance. In this paper, the types, features, and applications of the mainstream hybrid powertrain configurations on the market in recent years are summarized and the effects of different configurations on the comprehensive performance of HEVs are compared. Moreover, the technical routes for each hybrid configuration are highlighted, as configuration optimization methods have become a technical difficulty. In addition, the technological advances in the steady-state energy management strategy and dynamic coordinated control strategy for hybrid powertrain systems are studied. The optimization of the steady-state energy management strategy mainly involves assigning the working point and working range of each power source reasonably. However, with the increase in the complexity of optimization algorithms, real-time control of HEVs still needs to be improved. The optimization of the dynamic coordinated control strategy mainly focuses on the stability and smoothness of the dynamic process involving switching and shifting the working mode. The optimization of the dynamic control process for the system remains to be further improved. It is pointed out that the configurations and strategies should be optimized jointly to obtain a comprehensive improvement in the system performance. This paper provides an informative basis and technical support for the design and optimization of a hybrid powertrain system.

Integrated motion control and energy management of series hybrid electric vehicles: A multi-objective MPC approach

This paper considers the integrated motion control and energy management problems of the series hybrid electric vehicles (SHEV) with constraints. We propose a multi-objective model predictive control (MOMPC)-based energy management approach, which is embedded with the motion control to guarantee driving comfort. In addition, due to the slow response of the engine, it may cause excessive batter power when HEVs work in different conditions (e.g., uphill or sudden acceleration) with a certain request power; this implies the discharge current is too large. A battery current constraint is designed and incorporated into the MOMPC optimization problem and hence avoids the extra high charge-discharge current. This prevents potential safety hazards and extends the battery's life. Finally, the numerical experiments are performed to verify the proposed approach.

Nonlinear Optimal Control for Hybrid Electric Vehicles with Doubly Excited Synchronous Machine and AC/DC Converter

Nonlinear Optimal Control for Hybrid Electric Vehicles with Doubly Excited Synchronous Machine and AC/DC Converter

Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios

Co-optimization of vehicle velocity planning and powertrain control for plug-in hybrid electric vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. In this study, a real-time hierarchical effective and efficient co-optimization control strategy is designed for automated and connected PHEV to co-optimize vehicle velocity and energy management in urban driving scenarios. In the upper layer, the external traffic disturbance and powertrain characteristics are integrated into velocity planning via a Gaussian process (GP) model and a desired acceleration, respectively. In the power allocation layer, a double delayed Q-learning (DDQL) algorithm is employed to instantaneously optimize the power allocation for powertrain system based on the planned velocity. The feasibility and energy-saving effect of the proposed co-optimization strategy is verified through a traffic-in-the-loop simulator under various urban driving scenarios. The simulation results demonstrate that the integration of traffic lights, powertrain characteristics and speed prediction of preceding vehicle into velocity planning of PHEV can make vehicle velocity smoother, so as to improve fuel economy, driving comfort and traffic efficiency. As coupled with DDQL algorithm, our proposed co-optimization strategy can reach 97.31% energy economy of typical DP-based strategy but in a real-time framework.

Predictive energy management with engine switching control for hybrid electric vehicle via ADMM

This paper studies energy management (EM) of a power-split hybrid electric vehicle (HEV) equipped with planetary gear sets. We first formulate a mixed-integer global optimal control problem that includes a binary switching variable. Convex modeling, including the fuel model for a compound energy conversion unit, is then presented to reformulate the mixed-integer EM as a two-step program. For optimizing the engine switching and battery power decisions in the first step, we employ the alternating direction method of multipliers (ADMM) algorithm where the solution of the convex relaxation is used to initialize the non-convex problem. On the standard driving cycle, simulation results indicate that the ADMM based EM method saves 7.63% fuel compared to a heuristic method, and shows 99% optimality compared to a dynamic programming method, while saving three orders of magnitude in computing time. An ADMM combined model predictive control (ADMM-MPC) method is also developed that is suitable for receding horizon control scenarios. The ADMM-MPC method shows 5.28% fuel saving when implemented using a prediction horizon of 15 samples. Meanwhile, the mean computing time for MPC updates is 3.53ms. Our results demonstrate that the proposed ADMM is capable of real-time control.

Energy Management Control and Optimization for Hybrid Electric Vehicles

This Issue, Energy Management Control and Optimization for Hybrid Electric Vehicles, was set in motion over three years ago with the objective of addressing the challenges posed by energy management control and optimization in vehicle hybridization [...]

Hierarchical energy-saving optimization control of networked hybrid electric vehicles under urban Conditions

Hierarchical energy-saving optimization control of networked hybrid electric vehicles under urban Conditions

Distributed Nash Equilibrium Seeking for Aggregative Games With Directed Communication Graphs

One key factor affecting the distributed Nash equilibrium (NE) seeking in aggregative games is the unbalanced communication structure for multiple players. Although some results on seeking NE over undirected or weight-balanced graphs were established, how to address the distributed NE seeking problem over general directed communication graphs is still an outstanding challenge. This paper addresses the NE seeking problem for a class of aggregative games with general directed communication graphs. To achieve this objective, two new kinds of distributed discrete-time NE seeking algorithms are developed for aggregative games over fixed digraphs and time-varying digraphs, respectively. In particular, motivated by the heavy-ball method in optimization studies, a momentum term is introduced to the update law of the players’ actions and it is numerically verified that this momentum term accelerates the convergence of the proposed algorithms. For both strongly connected fixed graph and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> -strongly connected time-varying graph, it is theoretically proved that the actions of players will converge to the NE of aggregative games for the case of decreasing step-size implemented by the proposed NE seeking algorithms if the cost functions and the aggregation of players satisfy some certain conditions. Finally, the developed NE seeking algorithms are applied to the energy consumption control of plug-in hybrid electric vehicles (PHEVs), which demonstrates the effectiveness of the theoretical results.