Design Optimization and Optimal Control for Hybrid Vehicles

International audienceGrowing environmental and global crude oil supplies concerns are stimulating research on new vehicle technologies. Hybrid-electric vehicles appear to be one of the most promising technologies for reducing fuel consumption and pollutant emissions. Different types of hybrid-electric powertrains exist: from the mild-hybrid vehicle, equipped with a small electric motor, to the combined hybrid like the Toyota prius. This paper presents a parametric study focused on variations of the size of the powertrain components , and optimization of the power split between the engine and electric motor with respect to fuel consumption. To perform this optimization on a prescribed driving cycle (for instance, the New European Driving Cycle), a dynamic programming algorithm based on a reduced model is implemented. This simplified model allows a fast optimization with a fine parameterization of the controller: it furnishes the optimal power repartition at each time step regarding fuel consumption under constraints on the battery state of charge. The obtained results may be used to determine the best components of a given powertrain, for a prescribed vehicle cycle. The optimal split obtained thanks to dynamic programming algorithm can not be used directly on a vehicle as a real time control law, as the future can not be known in advance in normal driving conditions. To overcome this difficulty, we implement, as a real-time strategy, the Equivalent Consumption Minimization Strategy (ECMS): the battery being considered as an auxiliary reversible fuel reservoir, an instantaneous minimization of ECMS is performed. This control law is inferred from Pontryagin's Minimum Principle, where the Lagrange multiplier can be deduced from previous optimization results on given driving cycles. Offline optimization results and real-time control laws are compared for a realistic hybrid vehicle application. 2

Automated engine calibration of hybrid electric vehicles

We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle. The problem, which concerns minimization of fuel consumption under a NOx constraint, is formulated as an optimal control problem. By applying Pontryagin's maximum principle, this study shows that the problem is separable in space. In the case where the limits of battery state of charge are not activated, we show that the optimization problem is also separable in time. The optimal solution is obtained by iteratively solving the power-split control problem using dynamic programming or the Equivalent Consumption Minimization Strategy. In addition, we present a computationally efficient suboptimal solution, which aims at reducing the number of power-split optimizations required. An example is provided concerning optimization of engine management settings and power-split control of a parallel hybrid electric vehicle

Design optimization and optimal control for hybrid vehicles

International audienceIn the context of growing environmental concerns, hybrid-electric vehicles appear to be one of the most promising technologies for reducing fuel consumption and pollutant emissions. This paper presents a parametric study focused on variations of the size of the powertrain components, and optimization of the power split between the engine and electric motor with respect to fuel consumption. To take into account the ability of the engine to be turned off, and the energy consumed to start the engine, we consider a second state to represent the engine: this state permits to obtain a more realistic engine model than it is usually done. Results are obtained for a prescribed vehicle cycle thanks to a dynamic programming algorithm based on a reduced model, and furnish the optimal power repartition at each time step regarding fuel consumption under constraints on the battery state of charge, and may then be used to determine the best components of a given powertrain. To control the energy sources in real driving conditions, when the future is unknown , a real-time control strategy is used: the Equivalent Consumption Minimization Strategy (ECMS). In this strategy, the battery is being considered as an auxiliary reversible fuel reservoir, using a scaling parameter which can be deduced from dynamic programming results. Offline optimization results and ECMS are compared for a realistic hybrid vehicle application

Ultracapacitor Heavy Hybrid Vehicle: Model Predictive Control Using Future Information to Improve Fuel Consumption

This research is concerned with the improvement in the fuel economy of heavy transport vehicles through the use of high power ultracapacitors in a mild hybrid electric vehicle platform. Previous work has shown the potential for up to 15% improvement on a hybrid SUV platform, but preliminary simulations have shown the potential improvement for larger vehicles is much higher. Based on vehicle modeling information from the high fidelity, forward-looking modeling and simulation program Powertrain Systems Analysis Toolkit (PSAT), a mild parallel heavy ultracapacitor hybrid electric vehicle model is developed and validated to known vehicle performance measures. The vehicle is hybridized using a 75kW motor and small energy storage ultracapacitor pack of 56 Farads at 145 Volts. Among all hybridizing energy storage technologies, ultracapacitors pack extraordinary power capability, cycle lifetime, and ruggedness and as such are well suited to reducing the large power transients of a heavy vehicle. The control challenge is to effectively manage the very small energy buffer (a few hundred Watt-hours) the ultracapacitors provide to maximize the potential fuel economy. The optimal control technique of Dynamic Programming is first used on the vehicle model to obtain the \u27best possible\u27 fuel economy for the vehicle over the driving cycles. A variety of energy storage parameters are investigated to aid in determining the best ultracapacitor system characteristics and the resulting effects this has on the fuel economy. On a real vehicle, the Dynamic Programming method is not very useful since it is computationally demanding and requires predetermined vehicle torque demands to carry out the optimization. The Model Predictive Control (MPC) method is an optimization-based receding horizon control strategy which has shown potential as a powertrain control strategy in hybrid vehicles. An MPC strategy is developed for the hybrid vehicle based on an exponential decay torque prediction method which can achieve near-optimal fuel consumption even for very short prediction horizon lengths of a few seconds. A critical part of the MPC method which can greatly affect the overall control performance is that of the prediction model. The use of telematic based \u27future information\u27 to aid in the MPC prediction method is also investigated. Three types of future information currently obtainable from vehicle telematic technologies are speed limits, traffic conditions, and traffic signals, all of which have been incorporated to improve the vehicle fuel economy

Modeling and Optimization of a Plug-in hybrid electric vehicle

Today, the world is faced with a situation where new technologies have to be developed to decrease the dependence on natural non-renewable resources. Each day, as the demand for non-renewable resources increases, it puts great pressure on the scientific fraternity to develop new technologies that are aimed at reducing this dependence. Today\u27s road traffic plays a major part in the energy consumption worldwide. Hence it is imperative that we develop environmentally friendly solutions to this problem that arises in the transportation sector. Hybrid vehicle is one of the alternatives that can be seen as a viable solution to this energy crisis. The recent strides in the field of controls and optimization has led to the evolution of new control and optimization tools to target several simultaneous objectives in a plug-in hybrid electric vehicle. The control strategies primarily target the minimization of fuel consumption, while meeting the power demand and also enhancing the drivability. The present work deals with the backward and forward modeling of a Power Split Plug-in Hybrid electric Vehicle. The Power-split plug-in hybrid electric vehicle is a combination of both series and parallel hybrid electric vehicles. A power split hybrid derives its name from the power split device namely the planetary gear set. The planetary gear set splits the engine power, allowing for both series and parallel modes. The model developed incorporates the fuel consumption minimization principle viz. Equivalent Consumption Minimization Principle(ECMS). ECMS principle deals with assigning future fuel costs and savings to the actual usage of electrical energy. Thus, the present usage of electrical energy would mean that this energy has to be balanced by replenishment in terms of future fuel costs and the present usage of fuel for replenishment would be associated with future savings as this energy is available at a lower cost. The ECMS principle used for optimization provided the necessary minimization by maintaining the State of Charge of Renewable Electrical Storage System(RESS) within the prescribed limits. When properly designed by appropriately tuning the Charging and Discharging coefficients in the minimization strategy, we can optimize the vehicle performance over a given cycle, with the generation of power being intact and perhaps more to conform to the best emission standards in any part of the world

Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model

Control of Hybrid Electric Vehicles with Diesel Engines

This thesis is an approach to improve electric hybrid vehicles with respect to fuel consumption and to fulfil the future intended NOX emission regulations. It is based upon the conclusions made in the licentiate thesis Analysing Hybrid Drive System Topologies (Jonasson, 2002). The study in this thesis is restricted to a parallel hybrid vehicle equipped with a diesel engine, two electric machines and electrical energy storage and a model thereof is presented in the thesis. The choice to focus on the diesel engine is related to the high efficiency of this engine that also is the reason for the in later years increased market for diesel engines in conventional vehicles. Since one of the disadvantages, related to the diesel engine, are the nitrogen oxides (NOX) emissions, efforts is concentrated on reducing them, by means of the advantages of hybridisation. The reference vehicle in the simulations presented in this thesis is a Toyota Prius, an electric hybrid passenger car, which is available on the market today. As input for the combustion engine model, engine data from a diesel engine considered as state of the art 2004, has been used. The engine data is scaled to correspond to the engine size used in the Prius. It should be mentioned that the engine in the Toyota Prius is run on petrol. There are many possible parameters in the simulation model, which are adjustable; vehicle chassis parameters, engine, electric machine(s) and battery size and types, losses models, charging strategies and driver behaviour etc. A number of key parameters have been selected in this study: control strategy, NOX control by means of EGR (exhaust gas recirculation) and SCR (selective catalytic reduction), gear ratios and gearshift strategies and finally cylinder deactivation. The accuracy of the simulation model is ratified by means of measured data on the engine used in the simulation. Fuel consumption and NOX are determined by using look-up-tables based on measured data. The engine temperature, needed to determine the NOX conversion by means of SCR, is also received from a look-up-table. The simulation model is evaluated in the driving cycle ECE+EUDC. The results presented are chosen to illustrate the impact each individual parameter has on the behaviour of the hybrid vehicle, the fuel consumption and the emissions. The results from the simulations show that it is possible to pass the expected limit of the future Euro 5 NOX regulations, if NOX emission treatment with EGR and SCR is implemented. The price to pay for this action is to sacrifice some of the fuel savings that the hybridization brings. The result is nevertheless a vehicle with decreased fuel consumption compared with a conventional diesel powered vehicle, and a vehicle that passes the intended emission regulation

Plug-in Hybrid Electric Vehicle Control Strategies Utilizing Multiple Peaking Power Sources

Conventional automobiles operate with the use of internal combustion engines (ICEs) which run on fossil fuels as a source of energy. However, the conventional ICE provides poor fuel economy, as well as producing air pollutants. A Plug-in Hybrid Electric Vehicle (PHEV) has the potential to run solely on free energy with zero emissions as long as it operates within its all electric range. Active control techniques must be used in order to ensure optimum efficiency of the PHEV once the ICE is operated. The objective of the proposed research is to create a control strategy utilizing batteries as well as ultracapacitors suitable for a PHEV configuration. The control strategy will be evaluated through numerical models under several driving cycles as well as emergency maneuvers in order to ensure its effectiveness at reducing fuel consumption and improving engine efficiency

Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on the model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge that is verified using a vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and gives trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to two different sequential quadratic programs

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used