Hybrid Electric Vehicle Control Research Articles

Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on Heuristic Dynamic Programming

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.

Robust throttle control of Hybrid Electric Vehicle

In recent past years, growing carbon emission caused by vehicle and scarcity of oil resources gives attention of all researchers to investigate low pollution, improved fuel economy type vehicle such as Hybrid Electric Vehicle (HEV). In this paper nonlinear hybrid electric vehicle speed control using different control techniques has been employed to improve the efficiency of vehicle by throttle position control. The controller like Proportional-Integral-Derivative (PID), Artificial intelligence based fuzzy controller, robust controller (H∞) and their comparative performance analysis has been done on the basis of rise time, settling time, and zero steady error in MATLAB-Simulink.

Optimal Charging and Discharging Control of Plug-in Hybrid Electric Vehicles in a System-level Power Distribution Network

With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), coordinated charging and discharging of PHEVs has become an important issue in power distribution networks. To this end, we employ a multi-objective optimization model to formulate the problem of coordinated charging and discharging of PHEVs, in which the objectives of load shifting and total costs minimization are both investigated under system’s technical constraints. A hierarchical optimal algorithm combining the iterative water-filling-based algorithm and the consensus-based method is proposed to solve the multi-objective optimization problem. It shows that the proposed algorithm not only enhances stable performance for the power load but also guarantees low-cost satisfaction for the vehicle owners in the system-level power distribution network integrating physical power network with information network together. Numerical simulations are presented to demonstrate the effectiveness of the proposed algorithm.

Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy

Abstract This paper proposes a hierarchical optimization energy management strategy to suppress the battery aging in plug-in hybrid electric vehicles. In the first-level, a variable-threshold dynamic programming algorithm to distribute the power between the energy storage system and the engine is proposed. By adding supercapacitor to form the hybrid energy storage system, and using adaptive low-pass filtering algorithm, the power between the battery and the supercapacitor is distributed. To control the supercapacitor and battery to work within the capacity range, a power limits management module for redistributing the power between the engine, the supercapacitor and the battery is considered. The adaptive low-pass filtering algorithm and power limits management module constitute adaptive power allocation method in the second-level. After that, the rain-flow counting algorithm is applied in this paper to calculate battery aging cost. By using the rain-flow counting algorithm, the battery performances are analyzed, and the results show that the adaptive power allocation method can improve the battery service life by about 54.9% compared with the global dynamic programming algorithm. Considering the initial cost of the supercapacitor, the costs of battery aging, fuel consumption, electricity consumption, and management cost of retired batteries, compared with the global dynamic programming algorithm, the life cycle economy of the vehicle is improved by 12.4% under the proposed method.

Optimal Supervisory Control Strategy for a Transmission-Mounted Electric Drive Hybrid Electric Vehicle

This paper proposes a novel supervisory control for a parallel hybrid electric vehicles (HEVs) with a transmission-mounted electric drive (TMED). The proposed control includes an optimal power distribution algorithm that uses a modified equivalent consumption minimization strategy (ECMS) and an optimal mode determination algorithm based on the state machine framework. The control vector of the proposed ECMS is expanded to two degrees of freedom to determine the power-split ratios of the TMED HEV, and the cost function is re-defined in accordance with the target system. A new mode-transition algorithm is also proposed to prevent the occurrence of frequent mode transitions as well as improve fuel economy and drivability. Simulation results demonstrate the validity and performance of the proposed control algorithms.

Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems

With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), the coordinated charging of PHEVs has become an important issue in power distribution systems. This paper employs a multi-objective optimization model for coordinated charging of PHEVs in the system, in which the problem of valley filling and total cost minimization are both investigated under the system’s technical constraints. To this end, a hierarchical optimal algorithm combining the water-filling-based algorithm with the consensus-based method is proposed to solve the constrained optimization problem. Moreover, a moving horizon approach is adopted to deal with the case where PHEVs arrive and leave randomly. We show that the proposed algorithm not only enhances the stability of the power load but also achieves the global minimization of vehicle owners charging costs, and its implementation is convenient in the multi-level power distribution system integrating the physical power grid with a heterogeneous information network. Numerical simulations are presented to show the desirable performance of the proposed algorithm.

Torque Coordination Control of Hybrid Electric Vehicles Based on Hybrid Dynamical System Theory

In order to reduce the vibration caused by mode switching of hybrid electric vehicles (HEVs) and achieve smooth mode switching, the hybrid input and output automation (HIOA) model of power control system of a parallel HEV is established based on the theory of hybrid dynamical system (HDS). Taking the switching from electric drive mode to hybrid drive mode for example, the torque coordination control is considered, and the performance is compared with the method without the torque coordination by using a rule-based control strategy. The simulation results in AVL Cruise show that, on the premise of ensuring the fuel economy and the emission, the mode switching process becomes smoother with smaller torque fluctuation and better driving comfort by considering the torque coordination.

A Two-Level MPC for Energy Management Including Velocity Control of Hybrid Electric Vehicles

Todays cruise control systems try to keep a constant speed given by the driver without regarding the energy consumption. There is, however, possibilities to save energy by choosing the optimal velocity is neglected. Solving the underlying control problem for long distances with an algorithm that runs on a vehicle control unit is not straightforward, especially when it comes to hybrid electric vehicles (HEV). In order to overcome the computational burden, this paper presents a two-level model predictive control approach. It uses a sequential quadratic program (SQP) that comprises the total travel distance computing a target set for a lower layer that uses discrete state-space dynamic programming and Pontryagin's maximum principle to compute the optimal control for a short horizon. The computational efficiency is shown by a case study for an HEV passenger car with parallel powertrain, which reveals a cost advantage of up to 39% compared to a benchmark solution that exactly follows the velocity of the SQP while both approaches yield the same travel time.

Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) require supervisory controllers to distribute the propulsion power from sources like an engine and motors. Control concepts based on optimal control theories such as dynamic programming (DP) and Pontryagin’s minimum principle (PMP) have been studied to maximize fuel efficiencies. These concepts are, however, not practical for real-world applications because they guarantee optimality only if future driving information is given prior to the actual driving. Instead, heuristic rule-based control concepts are widely used in real-world applications. Those concepts are not only simple enough to be designed based on existing vehicle control concepts, but also allow developers to easily intervene in the control to enhance other vital aspects of real-world vehicle performances, such as safety and drivability. In this study, a rule-based control for parallel type-2 HEVs is developed based on representative control concepts of real-world HEVs, and optimal control parameters are determined by optimization processes. The performance of the optimized rule-based control is evaluated by comparing it with the optimal results obtained by PMP, and it shows that the rule-based concepts can achieve high fuel efficiencies, which are close, typically within 4%, to the maximum values obtained by PMP.

Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

An Optimized Real-Time Energy Management Strategy for the Power-Split Hybrid Electric Vehicles

This paper proposes a new real-time energy management strategy (R-EMS) to improve the fuel economy performance of the power-split hybrid electric vehicles (HEVs). Being different from most conventional optimization-based EMS, the R-EMS does not need priori information of the driving cycle and is used to the online control of HEV. The forward dynamic model of power-split powertrain is built based on the Prius MY10. At each instant, the proposed R-EMS tries to minimize the equivalent consumed power of the HEV, which is the weighted summation of gasoline power and battery output power. The equivalence factor of battery output power has a clear physical meaning that is the efficiency of gasoline energy transferred to battery energy. Another two coefficients are introduced to control the state of charge (SOC) of battery. By considering the engine torque and engine speed as two independent or dependent design variables, respectively, the 2-D R-EMS and 1-D R-EMS are formed. Several typical driving cycles are used to simulate the performance of the R-EMS, and the results show that the proposed R-EMS not only maintains the battery SOC but also saves the fuel consumption compared with the rule-based EMS.

Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle With Consideration of Battery Aging

In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.

Power Management Strategy for the 48 V Mild Hybrid Electric Vehicle Based on the Charge-Sustaining Control

To enhance the 48 V mild hybrid electric vehicle performance using a smaller capacity and lower voltage battery than the full hybrid electric vehicle, a novel power management strategy needs to be established that considers the characteristics and limitations of the components. This paper proposes a charge-sustaining control strategy as a ground principle of the 48 V hybrid electric vehicle control for managing the battery state-of-charge (SOC) to stay near the most efficient regime. The base efficiency characteristics of the component models including engine, motor/generator, and battery are determined in the form of efficiency maps using the powertrain analysis tool. Then the control strategy is formulated as a nonlinear optimal regulation problem that meets two conflicting control objectives, such as fuel efficiency improvement and state-of-charge maintenance. The optimal regulation problem implements a discrete-time Hamilton-Jacobi-Bellman approach. The proposed strategy is evaluated by comparing with the reference strategy applying the Dynamic Programming (DP), i.e. a global optimal result, under urban dynamometer driving schedule and worldwide harmonized light duty test cycle. Through the evaluation, the fuel efficiency of the proposed strategy with three different electrical loads is slightly deteriorated at most by 5.03 % from the DP results with staying within a desirable SOC. This suggests that the proposed strategy is operating very closely to global optimal performances.

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Hybrid electric vehicles (HEVs) provide a promising alternative to conventional engine-powered vehicles, with less emission and better fuel economy. This study proposes a hierarchical control for a power-split HEV over a driving cycle, featuring pre-trip optimisation and en-route speed adaption. Constraints including vehicle powertrain boundaries, road gradients, and speed limits, are taken into consideration. In the first stage, the HEV operating conditions, including the optimal vehicle SOC, speed profiles, and total driving time, are generated for the entire trip before departure. Based on the pre-trip results, the second stage adapts the vehicle speed for a short horizon when driving, while taking the safety spacing to the preceding vehicle into consideration, which acts as an indicator of actual traffic conditions and guarantees safe driving. Both optimisations are conducted under the distance domain for realising localisation in the optimal speed profile due to frequent changes in traffic conditions. An estimation of distribution algorithm is used to run the simulation so that the feasibility, robustness, and effectiveness of the proposed hierarchical control can be demonstrated.

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

Artificial neural network based adaptive control for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEV) have become vital for oil consumption reduction. They have not achieved their maximum potential due to control strategy limitations. Existing controllers are often tuned to achieve the best fuel economy for specific conditions. It is impractical to optimise a controller for every scenario. A control strategy for PHEVs using artificial neural networks (ANN) is presented. The advantages of implementing a controller using ANN include independence from drive cycle or user, precision and robustness, and updatable training set. Existing PHEV control strategies are used to model a base for city and highway driving. Simulation data was extracted to form an ANN training set, which was used to develop a new strategy that was better than existing ones. The controller was validated using different drive cycles. Furthermore, the sensitivity of ANN controllers is presented. The controller is also used to validate the charge depleting mode of PHEVs.

Artificial neural network based adaptive control for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEV) have become vital for oil consumption reduction. They have not achieved their maximum potential due to control strategy limitations. Existing controllers are often tuned to achieve the best fuel economy for specific conditions. It is impractical to optimise a controller for every scenario. A control strategy for PHEVs using artificial neural networks (ANN) is presented. The advantages of implementing a controller using ANN include independence from drive cycle or user, precision and robustness, and updatable training set. Existing PHEV control strategies are used to model a base for city and highway driving. Simulation data was extracted to form an ANN training set, which was used to develop a new strategy that was better than existing ones. The controller was validated using different drive cycles. Furthermore, the sensitivity of ANN controllers is presented. The controller is also used to validate the charge depleting mode of PHEVs.

Modelling and Control of PMSM Drive by Field Oriented Control For HEV

The Hybrid Electric Vehicles (HEVs) have the best of both worlds significant fuel saving, on demand power, lowered emission and quiet, smooth, emission-free electric motive power as well. Nowadays Permanent Magnet Synchronous Motor (PMSM) minimizes the drawbacks of the BLDC motor and it has high efficiency and their inherently high-power density they have been accepted as having great potential to compete with induction motor for HEV. Such that in this paper, The Field Oriented Control (FOC) method is implemented to control the speed and torque of the PMSM motor by using dq reference frame theory. The PI controllers are used to regulate the speed and the dq current to generate the gate pulses for switching of the inverter according to correction voltage. The whole system is simulated under the MATLAB Simulink environment. Results are carried out based on step change of speed and torque and we analyzed performance parameters such as gate pulse for the inverter, inverter output voltage and current, rotor angle, motor speed, dq current, and electromagnetic torque. Simulation results show that PMSM has a fast-dynamic response and tunable output speed and torque with minimum deviation.

Improved Implementation of Dynamic Programming on the Example of Hybrid Electric Vehicle Control

Abstract Hybrid electric drivetrains are an example of systems that can enable discrete switching between states with very distinct continuous behavior. For the design of these drivetrains, the required computation time for optimizing the control over a driving cycle is critical. To reduce computation time, we propose two methods to improve the implementation of dynamic programming by reducing the number of grid points that computationally demanding sub-models are evaluated for. The proposed methods do not require surrogate models and can be applied to arbitrary drivetrain topologies. A case study on a parallel, a series-parallel, and a power-split hybrid drivetrain is performed, and a reduction in computation time of up to 66% is shown.

Adaptive Tube-Based Nonlinear MPC for Economic Autonomous Cruise Control of Plug-In Hybrid Electric Vehicles

This paper proposes an adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of autonomous cruise control (ACC) systems. The proposed method utilizes two separate models to define the constrained receding horizon optimal control problem. A fixed nominal model is used to handle the problem constraints based on a robust tube-based approach. A separate adaptive model is used to define the objective function, which utilizes least square online parameter estimators for adaption. By having two separate models, this method takes into account uncertainties, modeling errors and delayed data in the design of the controller and guaranties robust constraint handling, while adapting to them to improve control performance. Furthermore, to be able implement the designed AT-NMPC in real-time, a Newton/GMRES fast solver is employed to solve the optimization problem. Simulations performed on a high-fidelity model of the baseline vehicle, the Toyota plug-in Prius, which is a plug-in hybrid electric vehicle (PHEV), show that the proposed controller is able to handle the defined constraints in the presence of uncertainty, while improving the energy cost of the trip. Moreover, the result of the hardware-in-loop experiment demonstrates the performance of the proposed controller in real time application.