Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

Automotive Stirling engine development program

The major accomplishments were the completion of the Basic Stirling Engine (BSE) and the Stirling Engine System (SES) designs on schedule, the approval and acceptance of those designs by NASA, and the initiation of manufacture of BSE components. The performance predictions indicate the Mod II engine design will meet or exceed the original program goals of 30% improvement in fuel economy over a conventional Internal Combustion (IC) powered vehicle, while providing acceptable emissions. This was accomplished while simultaneously reducing Mod II engine weight to a level comparable with IC engine power density, and packaging the Mod II in a 1985 Celebrity with no external sheet metal changes. The projected mileage of the Mod II Celebrity for the combined urban and highway CVS cycle is 40.9 mpg which is a 32% improvement over the IC Celebrity. If additional potential improvements are verified and incorporated in the Mod II, the mileage could increase to 42.7 mpg

Real-time predictive control for SI engines using linear parameter-varying models

As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included

Thermodynamic analysis, modelling and control of a novel hybrid propulsion system

Stringent emission regulations imposed by governments and depleting fossil fuel reserves have promoted the development of the automotive industry towards novel technologies. Various types of hybrid power plants for transport and stationary applications have emerged. The methodology of design and development of such power plants varies according to power producing components used in the systems. The practical feasibility of such power plants is a pre-requisite to any further development. This work presents thermodynamic analysis and modelling of such a novel power plant, assesses its feasibility and further discusses the development of a suitable control system. The proposed system consists of a hybrid configuration of a solid oxide fuel cell and IC engine as the main power producing components. A reformer supplies fuel gas to the fuel cell while the IC engine is supplied with a liquid fuel. The excess fuel from the fuel cell anode and the oxygen-depleted air from cathode of the fuel cell are also supplied to the engine. This gas mixture is aspirated into the engine with the balance of energy provided by the liquid fuel. The fuel cell exhaust streams are used to condition the fuel in the engine to ensure minimum pollutants and improved engine performance. Both, fuel cell and engine share the load on the system. The fuel cell operates on a base load while the engine handles majority of the transient load. This system is particularly suitable for a delivery truck or a bus cycle. Models of the system components reformer, solid oxide fuel cell, IC engine and turbocharger were developed to understand their steady state and dynamic behaviour. These models were validated against sources of literature and used to predict the effect of different operating conditions for each component. The main control parameters for each component were derived from these models. A first law analysis of the system at steady state was conducted to identify optimum operating region, verify feasibility and efficiency improvement of the system. The results suggested reduced engine fuel consumption and a 10 % improvement in system efficiency over the conventional diesel engines. Further, a second law analysis was conducted to determine the key areas of exergy losses and the rational efficiency of the system at full load operating conditions. The results indicate a rational efficiency of 25.4 % for the system. Sensitivity to changes in internal exergy losses on the system work potential was also determined. The exergy analysis indicates a potential for process optimisation as well as design improvements. This analysis provides a basis for the development of a novel control strategy based on exergy analysis and finite-time thermodynamics. A dynamic simulation of the control oriented system model identified the transient response and control parameters for the system. Based on these results, control systems were developed based on feedback control and model predictive control theories. These controllers mainly focus on air and fuel path management within the system and show an improved transient response for the system. In a hierarchical control structure for the system, the feedback controllers or the model predictive controller can perform local optimisation for the system, while a supervisory controller can perform global optimisation. The objective of the supervisory controller is to determining the load distribution between the fuel cell and the engine. A development strategy for such a top-level supervisory controller for the system is proposed. The hybrid power plant proposed in this thesis shows potential for application for transport and stationary power production with reduced emissions and fuel consumption. The first and second law of thermodynamics can both contribute to the development of a comprehensive control system. This work integrates research areas of powertrain design, thermodynamic analysis and control design. The development and design strategy followed for such a novel hybrid power plant can be useful to assess the potential of other hybrid systems as well

Modeling of internal combustion engine control processes

The control system has been suggested to convert the nonlinear model to a linearized plant model. Therefore, we characterized the engine model based on certain aspects and analyzed it accurately to fit in the system. Simulations were performed using the PID controller to regulate and maintain the output response while rejecting any input disturbance. An engine model of a control system shows an essential role in defining the correct parameters. Hence, Idle speed control is the principal of the highest confrontations for the automotive industry and developers as they were addressing many issues concerning engine at rest position and fuel-saving economy. We keep many experiments with changing values of the PID control system. Through Simulink graphs, we compared different results and were able to find the correct value for the Idle Speed Control of the engine model. Hence, this system control predicts the control change in the system for stable equilibrium. Via manual tuning of PID control parameters, we were able to linearize the plant model and determine the correct parameters of the plant model. This study also presents an AFR control for the engine model. The main contribution is that AFR regulation is reformulated as a tracking control for the required injected fuel. To obtain a better response, the output measurement is added to a predefined AFR control. The parameter tuning is straightforward, while better AFR control response and reduction can be achieved compared to the PID control. The AFR control has been studied for internal combustion engines based on the mean value model. The control design method based on nonlinear feedback control and their control performance has been investigated for different cases. The simulation results show that the controller applying the nonlinear feedback control could give satisfactory AFR regulation performances for our engine model with specific load disturbance

Advanced Neural Network Based Control for Automotive Engines

This thesis investigates the application of artificial neural networks (NN) in air/fuel ratio (AFR) control of spark ignition(SI) engines. Three advanced neural network based control schemes are proposed: radial basis function(RBF) neural network based feedforward-feedback control scheme, RBF based model predictive control scheme, and diagonal recurrent neural network (DRNN) - based model predictive control scheme. The major objective of these control schemes is to maintain the air/fuel ratio at the stoichiometric value of 14.7 , under varying disturbance and system uncertainty. All the developed methods have been assessed using an engine simulation model built based on a widely used engine model benchmark, mean value engine model (MVEM). Satisfactory control performance in terms of effective regulation and robustness to disturbance and system component change have been achieved. In the feedforward-feedback control scheme, a neural network model is used to predict air mass flow from system measurements. Then, the injected fuel is estimated by an inverse NN controller. The simulation results have shown that much improved control performance has been achieved compared with conventional PID control in both transient and steady-state response. A nonlinear model predictive control is developed for AFR control in this re- . search using RBF model. A one-dimensional optimization method, the secant method is employed to obtain optimal control variable in the MPC scheme, so that the computation load and consequently the computation time is greatly reduced. This feature significantly enhances the applicability of the MPC to industrial systems with fast dynamics. Moreover, the RBF model is on-line adapted to model engine time-varying dynamics and parameter uncertainty. As such, the developed control scheme is more robust and this is approved in the evaluation. The MPC strategy is further developed with the RBF model replaced by a DRNN model. The DRNN has structure including a information-storing neurons and is therefore more appropriate for dynamics system modelling than the RBF, a static network. In this research, the dynamic back-propagation algorithm (DBP) is adopted to train the DRNN and is realized by automatic differentiation (AD) technique. This greatly reduces the computation load and time in the model training. The MPC using the DRNN model is found in the simulation evaluation having better control performance than the RBF -based model predictive control. The main contribution of this research lies in the following aspects. A neural network based feedforward-feedback control scheme is developed for AFR of SI engines, which is performed better than traditional look-up table with PI control method. This new method needs moderate computation and therefore has strong potential to be applied in production engines in automotive industry. Furthermore, two adaptive neural network models, a RBF model and a DRNN model, are developed for engine and incorporated into the MPC scheme. Such developed two MPC schemes are proved by simulations having advanced features of low computation load, better regulation performance in both transient and steady state, and stronger robustness to engine time-varying dynamics and parameter uncertainty. Finally, the developed schemes are considered to suit the limited hardware capacity of engine control and have feasibility and strong potential to be practically implemented in the production engines

A Novel Learning Based Model Predictive Control Strategy for Plug-in Hybrid Electric Vehicle

The multi-source electromechanical coupling renders energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions, and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy saving improvement of PHEVs. To address these issues, a novel learning based model predictive control (LMPC) strategy is developed for a serial-parallel PHEV with the reinforced optimal control effect in real time application. Rather than employing the velocity-prediction based MPC methods favored in the literature, an original reference-tracking based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in LMPC based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin