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This paper proposed a hierarchical energy management strategy (EMS) for the charge station with mobility consideration of large-scale electric vehicles (EVs). The mobility characteristics of EVs is determined through the analysis of history data. In the upper layer, a day-ahead power trade planning to maximum the incomes from the power grid and the charging power for EVs. In the lower upper, a MFG-based real-time charging control strategy is designed to guarantee both the charging power performance and state of charge demand at the terminal time for next traveling. Simulation results show the effectiveness of the proposed hierarchical EMS.

This paper presents two learning-based approaches to solve the optimal energy management problem for hybrid electric vehicles. It will be shown that by applying a learning algorithm to the interpolation of value-function, which is an optimal approximate value-function in continuous state space, the discretization error can be rejected when performing dynamic programming. Extreme Learning Machine and Gaussian Process Regression are exploited as learning tools. Finally, numerical simulation results with a parallel HEV will be demonstrated to show the effort of value-function learning.

This paper developed a look-ahead horizon based optimal control scheme to jointly improve the efficiencies of powertrain and vehicle for hybrid electric vehicles (HEVs) with connectivity and automated driving. Both a speed planning strategy and energy management strategy is provided by the proposed approach. A constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-distance between ego vehicle and preceding vehicle. The optimal solution is derived through the Pontryagins maximum principle and verified in a traffic-in-the-loop powertrain simulation platform to show the effectiveness of the proposed approach.

This paper investigates an energy management problem of parallel hybrid electric vehicles (HEVs), which can be modeled as a finite horizon optimal control problem for the discrete dynamical systems. Taking the essential characteristics of plug-in HEVs into account, a logical-based optimization approach is applied to realize the equivalent energy cost minimization of the powertrain system. Then, based on semi-tensor product, an effective algorithm for obtaining an approximate optimal solution is proposed by using the logical network-based approach. Finally, simulation results are presented to illustrate and show the effectiveness of the proposed optimal control scheme and the corresponding algorithm.

Time-varying Boolean control network is firstly formulated. The controllability is investigated, necessary and sufficient condition is presented via controllability matrix ${\mathcal{C}^ * }$. Using ${\mathcal{C}^ * }$, the necessary and sufficient condition for set controllability is also obtained. Similar to time-invariant case, results for set controllability are used to solve some other control problems, including stabilization, observability, and output regulation.

This paper studies the optimal tracking control problem for continuous-time affine nonlinear systems and proposes a completely model-free approximate optimal tracking control design approach. This approach only uses measurement data collected from the trajectories of the system in real time to learn the optimal tacking control. At first, a new tracking policy iteration algorithm is developed based on the integral reinforcement learning technique. Then, the algorithm is implemented based on the actor-critic structure, where the critic neural network and the actor neural network are updated iteratively. Finally, simulation results are provided to show the efficiency of the method.

In this paper, a formation scaling maneuver problem of autonomous surface vehicles (ASVs) with uncertain dynamics and bearing constraints is investigated. Based on graph theories, the bearing rigidity theory, dynamic surface and adaptive neural control, we propose a new adaptive formation scaling maneuver control scheme for ASVs. Two virtual leaders are programmed to generate a bearing constrained target formation with desired yawing. Control inputs combined with adaptive laws are designed using inter-neighbour bearings, neighbouring states and filtered virtual signals of neighbours. It is shown that if the augmented framework is infinitesimal bearing rigid, desired formation scaling maneuver of ASVs can be achieved with proposed controllers. And the formation sizes can be scaled only by two virtual leaders without changing control inputs of followers. Compared with the existing results, our developed scheme reduces weights of each channel to a parameter when obtaining robustness against model uncertainties of ASVs. Simulations and comparison results are provided to illustrate the effectiveness of theoretical results.

This study presents a control Lyapunov function (CLF) based nonlinear control design method for the intake and exhaust manifold pressure tracking problem with three control input: throttle valve opening, Exhaust Gas Recirculation (EGR) and Variable Valve Timing (VVT). Firstly, the optimal set-points are designed by solving the stationary optimization problem of minimize the fuel consumption and meanwhile to satisfy the coefficient of variation of IMEP (Indicated Mean Effective Pressure) constraint. Secondly, the Recursive Least Square (RLS) algorithm and Polynomial Fitting Method (PTM) are adopted to obtain an optimal estimation for the model parameters. Finally, a CLF based nonlinear control design method is applied to ensure the fast pressure tracking performance, and meanwhile to guarantee the combustion stability. Finally, the model and controller has been tested in on a real gasoline engine, and the experiment results demonstrate the performance of the control- oriented models.

As the Unmanned Surface Vessels (USV) having been applied in diverse and complex environments, it is extremely important to improve autonomous navigation. Considering this background, a greedy navigation and subtle obstacle avoidance algorithm is proposed on the basis of actor-critic architecture to achieve the goal with very little training cost. Markov process is established elaborately to fit the kinematics equation and the reward function with behavioral priori provides benefits in both training and testing. Compared to the analytical approach, the proposed algorithm has the features of conciseness, adaptability and extendibility. Four different scenarios are designed and adopted to demonstrate the effectiveness and practicalbility of our algorithm.

In this paper, a real-time energy management for parallel hybrid electric vehicles (HEVs) to improve fuel efficiency is presented. A two-level optimal controller using model predictive control (MPC) scheme is designed to realize the energy management. The first level is designed to decide the operating mode with a simple rule-based block. In order to realize the total minimization of fuel and power, a nonlinear optimal control problem based MPC is formulated to generate the optimal power split and gear ratio schedule in the second level. The multiple shooting algorithm is introduced to decouple the dynamic constraints. After that the proposed optimal problem is converted into a nonlinear optimization problem with the extra variables. Then the problem is solved using sequential quadratic programming (SQP) method. A virtual traffic simulation platform CarMaker is built to emulate the real driving conditions. A co-simulation platform can be designed with MATLAB/Simulink and CarMaker which is called traffic-in-theloop-platform (TILP). The proposed controller can implement in the TILP platform without relying on predefined route.

This paper presents a new architecture of real-time HEV’s energy management problem under a V2V and V2I environment using policy-based deep reinforcement learning. The ideal energy management controller that minimizes HEV energy costs needs to run engines most efficiently in the whole running considering battery SoC. The controller needs to predict the future vehicle speed and plan the power distribution to achieve it because the thermal efficiency of engines is more efficient when its rotational speed is higher. The future vehicle speed has relationship with connectivity information such as the behavior of the car in front, the traffic light signals, crowd of cars, and so on. This paper assumes the connectivity environment in the future and applies proximal policy optimization (PPO) [5] that is known as policy-based deep reinforcement learning algorithm to achieve the optimal power distribution predicting the future behavior by using connectivity information. In addition, this paper shows that locating the local controller in the reinforcement learning loop enables the AI controller to learn robustly. The local controller corrects against an exploration that is obviously not optimal or doesn’t satisfy the constraints.

This paper presents a stochastic model predictive control (SMPC) design scheme for gasoline engines with exhaust gas recirculation (EGR) by considering the stochastic distribution of indicated mean effective pressure (IMEP) as constraint to avoid abnormal combustion during transient conditions. Firstly, a MPC problem is formulated to minimize the system state tracking error based on a second order state space model. Then, the SMPC problem under stochastic IMEP distribution constraint is formulated. Moreover, in order to make the problem solvable, a scenario-based optimization approach is applied to transform the stochastic optimal control problem into a deterministic optimal control problem. Finally, the deterministic optimal control problem is solved by multi-shooting based sequence quadratic programming (SQP). The simulation validation shows that the proposed SMPC scheme can avoid misfire during the tip put transient condition while guaranteeing the system states tracking performance without depending on the exhaust manifold pressure sensor.

Real-time energy management for parallel hybrid electric vehicles (HEVs) is crucial to improve fuel economy and reduce pollution. While real-time solutions can only insure sub optimality. In general, the offline dynamic programming (DP) is consider as the strategy to obtain the globally optimal solution for a given driving cycle. In this paper, the simplified vehicle mathematic model is formulated based on Toyota parallel HEV system. This paper proposes a real-time energy management strategy on optimizing the power-split to reduce the price of fuel and power with model predictive control (MPC), using multiple shooting frame and seqential quadratic programming (SQP) algorithm to deal with the proposed nonlinear optimal problem. In addition, this paper demonstrates the DP results for the same optimization problem and vehicle system, with the velocity curve deriving from the real-time solution case. Finally, by comparing the results of two cases, the performance of proposed MPC strategy can be determined.

Combustion phase control is of great importance in spark ignition (SI) engine researches since it affects combustion qualities, such as fuel efficiency, combustion variation, and knocking. Keeping the combustion phase at the optimal reference value considering physical constraints, knock for instance, is challenging due to the engine transient operation, drift of the optimal controllable variable values and varying physical constraints. To address this issue, this research presents an on-board map-learning scheme. Firstly, taking the crank angle of 50% mass burnt (CA50) as the combustion phase indicator, a 3-dimensional mapping from CA50, manifold pressure, and engine speed to thermal efficiency, knock intensity, and spark advance (SA) is constructed. Secondly, a trilinear interpolation model and a stochastic gradient algorithm are employed to learn the map by iterative updates, which reduces computational complexity and the use of memory. Thirdly, the resulting map generates the optimal CA50 reference (CA50*) under knock constraint, and then a statistical feedback controller tracks CA50* by altering SA. Finally, experimental validations carried out on a six-cylinder SI gasoline engine demonstrate that the presented scheme contributes to faster response of transient operation condition and the on-board learning loop compensates the map drift.

This paper summarizes the framework of applying probability-constrained optimal control for combustion engines. The probabilistic constrained control problem is formulated in engine, after which, both the data driven-based methodology and explicit model-based are briefly reviewed to address the problem. Two study cases are given as the explanation for the applications of probability-constrained optimal control in engine system. The first study case is knock probability constrained thermal efficiency optimization. An iterative learning-based control strategy is proposed to solve the problem by combining extremum seeking method and likelihood-based method. On the other hand, scenario approach is applied to solve torque tracking control with combustion phase probabilistic threshold.

This paper presents a recursive constructing ap-proach to Lyapunov function with optimality for a class of nonlinear systems. The targeted systems are formulated as cascaded system with triangular structure. For this class of nonlinear systems, stabilization problem has been a typical issue and solved by recursively constructing Lyapunov function using so-called back-stepping process. However, as is well known, this constructive design of feedback stabilizing control law is usually lacking time response performance due to the attention of controller design focuses stability only. The presented design approach in this paper puts an optimality into the recursive design process by targeting an approximate solution of Hamiltonian equality. It has been shown that at each stage of the recursive design a Lyapunov function that guarantees optimality can be obtained approximately by policy iteration. Finally, numerical examples are shown to demonstrate the design process.

A traffic-in-loop powertrain simulation framework, aiming at real time driving simulation validation, is presented in this paper. Traffic scenario and powertrain model are built in different platforms with capability of information interchange. This bidirectional co-simulation is capable to imitate I2V (infrastructure to vehicle)communication, connected vehicle communication and to capture driver behavior under stochastic traffic environment. With these real driving information, interaction influence between traffic and powertrain could be reflected online. An application example of optimal control design for diesel engine with after-treatment system is illustrated. Simulation validation on deterministic and stochastic traffic scenario are conducted, respectively. It could be concluded the necessity of onboard control scheme for powertrain in real-world traffic consideration.

This paper presents an investigation of control variables, which are throttle angle (TA), spark advance (SA) and variable valve timing (VVT), on combustion parameters of a commercial gasoline engine under lean combustion. The parameters considered in this work are the location of 50% of mass fraction burned (CA50) and the location of peak pressure (LPP). These parameters are calculated online using the algorithm that can be implemented on MATLAB/Simulink platform and then complied in C-code and real-time executed on the dSPACE1006. The lean combustion experiments are conducted on the engine test bench consists of a six cylinders gasoline commercial engine with a low inertia dynamometer. Based on the experimental results, the effects of considered control variables on combustion parameters can be investigated. Additionally, we can use this information for improvement of lean combustion control performance and reduce the combustion variability.

In this paper, optimization problem of after treatment system is investigated for diesel engines with lean NOx trap (LNT). First, a control-oriented LNT model is developed based on energy and mass balancing rules. Then, the optimization problem is formulated as a dynamic programming (DP) problem under developed dynamical model constraint and a trade-off between fuel economy and NOx emission is considered in the cost function. To demonstrate the optimal control obtained by solving the proposed DP problem numerically, the parameters of LNT model are identified with a GT-power diesel engine simulator, and numerical simulation results with comparison to a conventional rule-based control strategy are shown by using the identified model.

The combustion phase control is a significant research topic in spark ignition (SI) engines since it affects the fuel efficiency, combustion variability and knocking. Managing the combustion phase, which can be set in the crank angle of 50% mass burnt (CA50), at the optimal reference value where the maximal thermal efficiency can be obtained is challenging due to the engine transient operation, the cyclic variability of combustion process and the optimal operation point drift caused by engine aging, fuel quality, etc. This research addresses this problem in two loops: an outer loop providing the optimal CA50 reference (CA50∗) and an inner loop tracking CA50∗ by managing the spark advance (SA). CA50∗ is obtained by looking up a 3-dimensional map that can be updated by the stochastic gradient-based on-board map learning algorithm. Then a SA feedforward-feedback controller is designed to track CA50∗. The closed loop is of benefit to the fast response of transient operation condition and the on-board learning loop compensates the map drift. Experimental validations of the proposed scheme have been carried out on a six-cylinder SI gasoline engine test bench at transient operation mode.

This paper presents an H ∞ control scheme for gasoline engines based on a mean-value model. First, a linearized model based on a mean-valve nonlinear model of the engines is proposed that represents dynamical behavior under the multi-input excitation of the fuel injection and the throttle opening around an operating condition. Then, for the feedback control system to regulate of the speed and the air-fuel ratio, the mixed sensitivity function shaping method is applied to the feedback controller design. Furthermore, the parameter identification of the linearized model is shown with experiment results obtained on a full-scale engine transient control testbench. With the identified model, the frequency property and the time response of the control system is demonstrated by numerical simulation.

Current automotive engine developments have been driven by improving vehicle fuel consumption. Various ideas have been explored to reduce the fuel consumption of vehicle including variable cylinder engine. The benefit is given by the pumping loss reduction but the scope to reduce mechanical pumping loss has still remained. In this paper, the estimation of fuel consumption on a multi-crankshaft engine in which a part of the seperated crankshafts is deactivated is described.

Combustion phase and residual gas fraction (RGF) are two important factors affecting fuel efficiency and emission, however, which suffer from the cyclic variation of combustion process. To let combustion phase and RGF track their set points and limit the variances, multivariate statistical criterion based controller design is synthesized in this study. For spark ignition gasoline engines, spark advance and intake variable valve timing are considered as inputs, CA50 and RGF are controlled parameters where CA50 represents combustion phase, cyclic evolution model is identified at a steady-state condition, then Hotelling's T2 test is applied to check whether CA50 or RGF needs adjustment, while the gain of controller is still designed based on discrete linear model. Finally, the proposed controller is validated on a 6-cylinder 3.5L Toyota gasoline engine and the effect of statistical criterion is analyzed in the experiments.

This paper proposes a control scheme for the energy management of plug-in hybrid electric vehicles (HEVs). Consider the urban driving case, the traffic conditions are treated as stochastic processes of the driver demands. Taking the essential characteristics of plug-in HEVs into account, a logical-based optimization approach is applied to realize the equivalent energy cost minimization of the powertrain. The stochastic design approach provides the solution of a finite horizon optimal control problem. The performance of the proposed control scheme is evaluated by means of a full-scaled hybrid electric vehicle (HEV) simulator.

This paper investigates the average infinite horizon optimal control problem for Boolean control networks (BCNs). Based on the semi-tensor product of matrices and Jordan decomposition technique, an optimality equation for the average infinite horizon problem of BCNs is presented. By resorting to Laurent series expression, a policy iteration algorithm, which can find the optimal solution in finite iteration steps, is deduced. As applications, the output tracking problem for BCNs and the intervention problem of cAMP receptor protein are investigated.

This paper proposes an experimental comparison between two optimal controllers for automotive spark ignition engines, including an gain-scheduled linear quadratic regulation (LQR) controller and model predictive controller (MPC). The aim of the study is to highlight the control effects between the LQR and MPC scheme on the specific engine control problem, and provide a reference for the future controller design. The control problem is formulated to ensure the fast torque tracking performance, and meanwhile to improve the thermal efficiency by reducing the pumping loss. The nitrogen oxide emission is chosen as constraint. The experiment were implemented on the full-scale gasoline engine, and the experiment results demonstrate the performance of the proposed optimal control designs.

This paper presents a multi-variable optimal controller design based on model predictive control (MPC) scheme for automotive gasoline engines. The optimal control aims to achieve fast torque tracking with the lower pumping loss, by tuning the throttle valve angle and the intake valve timing. To this end, the nonlinear engine model was built based on the mean-value modeling theory and polynomial technique, and MPC is designed with the derived linearized model. The control performance was validated on a full scale gasoline engine in real time. The experimental results demonstrate the effectiveness of the proposed control scheme.

Calibration in combustion phase control is an effective way to get sophisticated engine performance, but is only workable by analyzing offline data or running engine in test mode. When engine is aged or runs at unfamiliar situations, traditional calibration method cannot promise the same performance as before. To improve calibration technique, an online calibration method for combustion phase control is presented, which also works when engine is running in driving operating condition. Based on bilinear interpolation algorithm, online calibration problem is converted to parameters estimation issue, then stochastic gradient descent algorithm is utilized to estimate parameters by iteratively updates. Finally, the proposed strategy is verified on a gasoline spark ignition engine.

This paper presents the simple adaptive air-fuel ratio control for lean combustion of commercial spark-ignition (SI) engines. In this research, we focus on the strategy for the direct injection fueling control while the spark advance (SA) angle is regulated at maximum brake torque (MBT) by the calibrated look-up table. The experiment conducted on the commercial SI engine show that the proposed adaptive control method has high performance for fueling control on the lean combustion region. Based on this control strategy, we can control the equivalent air-fuel ratio (λ) up to 1.5 without the engine modification. Additionally, the proposed controller shows an effective performance for the disturbance rejection.

This paper presents a Lyapunov-based feedback design approach to set-point regulation problem of combustion engines. To enable the design approach, a mean-value model of the gasoline engine is proposed by experiment-based calibration of the torque generation. Then, a state feedback control law is deduced in the fashion of Lyapunov stability, and the control law is extended to the case of adaptive control. To demonstrate the proposed control schemes, simulation-based validation results will be shown finally with two stages, a control test in stand-alone engine and a driving scenario under energy management of hybrid electric vehicle (HEV).

This paper proposes a model-based optimal control approach for automotive gasoline engines to achieve the torque tracking control with lower pumping loss. The reduced engine dynamic model involving multi-input-multi-output framework is derived based on the mean-value model theory and the polynomial fitting technique. Then the optimal controllers based on the gain-scheduled LQR control scheme is designed for the high efficient torque tracking purpose. Simulation validations are conducted to validate the effectiveness of the proposed controller.

This paper investigates the discounted infinite horizon optimal control problem for the stochastic multi-valued logical dynamical systems with finite states. After giving the equivalent descriptions of the stochastic logical dynamical system in terms of Markov decision process, the infinite horizon optimization problem is presented in an algebraic form. Based on the semi-tensor product of matrices and the increasing-dimension technique, it is proved that the optimal stationary policy is obtained by a finite horizon value iteration process, and an exact horizon length estimation for the finite horizon approach is derived. As an application, the optimization problem of Human-machine game is investigated.

In this paper, combustion phase optimization is analyzed and ignition timing of SI engines is controlled to maximize the fuel efficiency, in which the optimum set point of combustion phase and moving average based control strategy are discussed. To extract the fuel efficiency information from combustion, in-cylinder pressure sensor is available to calculate combustion phase and IMEP. Then, CA50 is employed as combustion phase indicator, and gradient descent method is utilized to seek the set point of CA50 that ensures the maximum statistical IMEP, satisfying the probability constraint with least cycles. On the evaluation of consecutive in-cylinder pressure traces, moving average based feedback controller is designed to track the set point of CA50 by adjusting ignition timing. Finally, the proposed strategy is validated by the experiment on a SI gasoline engine.

In this paper, a cyclic event based NOx model is developed and validated for targeting the challenges of NOx control which is primarily dependent on highly complex combustion phenomena and cyclic coupling of residual gas and energy, specially in transient mode. The model input parameters are estimated from two discrete time models with Kalman filter estimation, considering the effects of cycle-to-cycle coupling of residual gas and energy. First model is developed based on residual gas cycle-to-cycle coupling dynamics and second based on cyclic energy dynamic due to energy carrying by residual gas coupling. The in-cylinder adiabatic temperature and oxygen equilibrium concentration are then calculated using both model on cyclic event based. Finally, the cyclic transient NOx model is calibrated and validated on full scale of gasoline engine to illustrate the transient response of NOx formation.

In internal combustion engines, one of the most important factors influencing performance is the combustion phase, which is represented by the crank angle of 50% burnt (CA50) in this paper. In order to optimize the performance, which is represented by the indicated mean effective pressure (IMEP), CA50 should be controlled to track its optimal reference. IMEP can be considered as a function of CA50 coupled with stochastic noise, and thus leads to a stochastic optimization problem. A gradient based stochastic approximation algorithm is adopted to solve this problem. To estimate the gradient, a sample based probabilistic gradient approximation approach is developed, in which the probabilistic guarantee of estimation accuracy can be attained by adjusting the number of samples adaptively. Finally, the experimental validation is conducted on a SI gasoline engine test bench.

This paper proposes a scenario-based stochastic optimal control strategy, considering the stochastic driver behaviors, to deal with energy management issue for parallel HEVs. Firstly, after modelling the dynamic system of parallel HEV including both mechanical and electrical systems, a stochastic model predictive control(SMPC) problem with average constraints is proposed for energy management issue regarding the demaned torque in the prediction horizon as stochastic variable. Moreover, in order to make the proposed problem solvable, two scenarios are chosen and weighted based on the known conditioned transition probability distribustion of demanded torque to transform the original problem into equivalent deterministic nonlinear model predictive control(NMPC) problem. The formulated equivalent problem is solved by employing the Continuation/GMRES algorithm. Afterwards, on-line learning algorithm for updating conditioned transition probabilities of demanded torque is developed since the drive behavior varies as route and environment change. Finally, validation simulation is carried on by a HEV simulator established in the GT-Suite Software.

In this paper, a mapping calibration method based on D-optimization algorithm is proposed and applied to the air mass calibration problem. The first procedure is to carry out a preliminary experiment around the main operating domain, and the polynomial regression model is chosen by minimum corrected Akaike Information Criterion (cAIC) criteria. The second procedure is to calculate D-optimal design of experiment for the given model. The experiment results on a full-scale engine test bench is demonstrated to confirm that the proposed method can provide the precise model.

In this paper, we consider the optimal control problem for the residual gas fraction in internal combustion engines. After discussing the stochastic characteristic of distribution of residual gas fraction under different variable valve timing (VVT) degrees, a multi-valued logical dynamical model is developed to capture the stochastic evolution of RGF from one cycle to the next under the effect of VVT. An algebraic form of dynamic programming algorithm is given to solve the optimal control problem for the residual gas fraction. Finally, to demonstrate the performances of the proposed control scheme, the experiment is expressed.

The paper proposes a nonlinear adaptive control strategy to deal with idle speed control for spark ignition (SI) engines. Focusing on rejecting the load disturbance during idling operation, an adaptive speed controller for throttle, combined with a delay compensation by spark advance (SA), is designed based on the nonlinear mean-value engine model. To deal with the performance interaction of the air-fuel ratio to the idle speed, a novel fuel injection control strategy is integrated with the speed control loop. The robustness of the idle speed control is demonstrated by numerical simulation first. Experimental testing on an engine test bench demonstrates the effectiveness of the control strategy.

In this paper, the optimal control scheme for residual gas fraction is proposed considering the stochastic property in engine. Initially, the receding horizon optimal control problem for the stochastic logical dynamical systems with finite states is considered. Based on transition probability and semi-tensor product of matrix, a succinct algebraic expression of dynamic programming algorithm is derived to solve the receding horizon control problem. Then, an optimal controller designed to reduce the variation of residual gas fraction in the framework of stochastic logician dynamical model, in which variable valve timing is taken as the control actuator. Validation results are demonstrated which conducted on a full-scaled gasoline engine test bench.

The oil crisis and strict emission legislation have greatly motivated the development of internal combustion engine technology. To improve fuel economy performance and reduce emissions, engine transient control has attracted wide research interests. However, engine system is a nonlinear physical plant with constraints on itself and actuators, and also it is a sophisticated control system involving many control loops to achieve single or multiple objectives. Therefore it is really a challenging issue on the transient control of the engine. In recent years, receding horizon control (RHC) was gained much attention in the field of engine control, owing to its advantages that it can explicitly tackle the constrained optimization and multi-variable control problem. However, a remarkable drawback of RHC is the heavy computation load for on-line optimization algorithm, especially for the nonlinear control system. Indeed, this bottleneck restricts its practical implementation on the industrial electronic controller of a fast control system for a long time. This tutorial paper proposes a systematic receding horizon controller design approach for the transient control applications of the gasoline engines. Two independent RHC-based tracking controllers aimed to achieve the engine torque and speed tracking control are designed, respectively. The control oriented model is derived from the mean-value model of gasoline engines, meanwhile the integrator of the tracking error is embedded to improve the tracking accuracy. All the proposed controllers are verified in real-time on a full-scale gasoline engine and the on-line optimization algorithm for RHC adopts C/GMRES method, which can provide an approximately optimal solution by solving the linear equation instead of the Riccati differential equation. The experimental results demonstrate a large potential for improving the engine transient control performance with RHC scheme.

This paper suggests the method to seek the optimal variable valve timing (VVT) which maximize indicated mean effective pressure in combustion engine. Using the golden section search and D-optimal design, the VVT mapping is conducted in different engine operating conditions. Finally, the experiment result is presented in order to show the efficiency of the map.

Nowadays, predictive control which applies a model to predict the future system behavior is suitable for power management design in parallel HEV. However, both vehicle and driver should be considered together for predicting the system dynamics in the future, especially the driver behavior. In this paper, the driver's action, torque demand, is regarded as stochastic variable which is modelled as Markov process based on known conditioned probability distribution obtained from driver's statistical behaviors. Then, the control maps are obtained by off-line optimization algorithm under consideration of vehicle dynamics and the stochastic future torque demand. With cost function evaluating the equivalent energy consumption, the stochastic optimization problem with chance-constrained is solved by combining scenario approach and vector quantization method. Numerical simulation-based vase studies are demonstrated to validate the proposed design scheme finally.

This paper introduces applications of a numerical method for realizing real-time optimization for energy management of plug-in hybrid electric vehicles (PHEVs). Without previous information of driving route, on-line power-split decision of driver demand power is formulated as nonlinear receding horizon control (RHC) problem. Fuel economy optimization for both of power-split and parallel PHEVs is investigated. The Continuation/GMRES (generalized minimum residual) algorithm is applied to solve the proposed nonlinear RHC problems for real-timeness. Testing results of the proposed strategies are demonstrated by using GT-Suite HEV simulators.

Bus fuel economy is closely related to driver's habits and driving conditions. How to efficiently arrange drivers, buses and routes with better fuel economy is a difficult problem for bus companies. This paper aims to propose a statistical evaluation model for this problem. The features of bus configurations, driver operations and driving routes were analyzed, and 6 key factors were defined to represent their effects on fuel economy, which are bus design optimal velocity, bus design optimal acceleration, driver desiring velocity, driver desiring acceleration, mean velocity of bus route and mean acceleration of bus route. Based on the power balance of driver-bus-route, the problem of driver-bus-route optimization can be depicted by driver, bus and route statistical points. The sum of weighted distance of three points can be set as the evaluation index of driver-bus-route arrangement. This statistical valuation model was finally applied with the monitor data from 11 drivers, 2 bus lines and 2 typical buses for more than one year. The data analysis results show that the sorting result of evaluation index is consistent with fuel economy and the proposed evaluation index can effective predict the fuel economy level of driver-bus-route arrangement. By comparing the evaluation index of the statistical evaluation model, a relatively optimal arrangement of bus-driver-routes for fuel saving can be achieved.