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

Energy management for vehicular electric power systems

The electric energy consumption in passenger vehicles is rapidly increasing. To limit the associated increase in fuel consumption, an energy management system has been developed. This system exploits the fact that the losses in the internal combustion engine vary with the operating point, and uses the possibility to temporarily store electric energy in a battery, such that electric energy is only produced at moments when it is cheap to generate. To come to a practically applicable solution, a vehicle model is derived, containing only the component characteristics relevant for this application. The energy management problem is formulated as an optimization problem. The fuel consumption over a driving cycle is minimized, while respecting physical limitations of the components and maintaining an acceptable energy level of the battery. Several optimization methods are studied to come to a solution. The dynamic optimization problem is solved using Dynamic Programming. After rewriting it as a static optimization problem and approximating the cost function by a quadratic function, the problem is solved using Quadratic Programming, which requires less computation time. A real-time implementable strategy has been derived from the Quadratic Programming problem, that does not require a prediction of the future driving cycle. This strategy compares the current cost of producing electric energy with the estimated average cost. By adapting the average cost based on the energy level of the battery, it is ensured that the battery energy level will remain around the desired value. Simulations show that a fuel reduction up till 2% can be obtained on a conventional vehicle without major hardware changes. Higher reductions are possible on the exhaust emissions. To predict and explain the amount of fuel reduction that can be obtained with a given vehicle configuration, a set of engineering rules is derived based on typical component characteristics. Their results correspond reasonably well with the simulations. An advanced power net topology is studied which contains both a battery and an ultracapacitor that are connected by a DC-DC converter and a switch. Because of the increased complexity, this system is modeled using linear and piecewise linear approximations of the component characteristics, such that the energy management problem can be casted as a Linear Programming problem. The discrete switch makes it a Mixed Integer Linear Programming Problem. A realtime strategy, similar to the strategy for the conventional power net, has been derived. The addition fuel reduction that is obtained with the dual storage power net is small, because the maximum profit that can be obtained with an ideal lossless battery is not much higher than with a normal battery. Subsequently, hybrid electric vehicles are studied that use an Integrated Starter Generator which can be used for generating electric power and for vehicle propulsion. Several conSummary figurations are studied with respect to their potential fuel reduction. Configurations that enable start-stop operation of the engine obtain a much higher fuel reduction, up to 40%. The controllers are tested in real-time on a Hardware-in-the-Loop environment, where a vehicle simulation model is combined with existing electric components. It is shown that the electric power setpoints provided by an energy management strategy can be realized in practice

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

[EN] In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. 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Control algorithms for e-car

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.The aim of this work is to design and implement energy consumption optimization control algorithms for electric vehicle. The main objective is to optimize the power-split-ratio between the main power source (batteries) and the super-capacitors during the driving cycle. The driving power profile is estimated and predicted using 3D geographic data and vehicle model. In the first part, vehicle components modelling is introduced. Then, moving average based algorithm and dynamic programming algorithm are presented. Simulations and analysis are provided to show algorithms' benefits. In the last part, Java implementation and also Android operating system application are described.

Predictive Energy Management in Connected Vehicles: Utilizing Route Information Preview for Energy Saving

This dissertation formulates algorithms that use preview information of road terrain and traffic flow for reducing energy use and emissions of modern vehicles with conventional or hybrid powertrains. Energy crisis, long term energy deficit, and more restrictive environmental protection policies require developing more efficient and cleaner vehicle powertrain systems. An alternative to making advanced technology engines or electrifying the vehicle powertrain is utilizing ambient terrain and traffic information in the energy management of vehicles, a topic which has not been emphasized in the past. Today\u27s advances in vehicular telematics and advances in GIS (Geographic Information System), GPS (Global Positioning Systems), ITS (Intelligent Transportation Systems), V2V (Vehicle to Vehicle) communication, and VII (Vehicle Infrastructure Integration ) create more opportunities for predicting a vehicle\u27s trip information with details such as the future road grade, the distance to the destination, speed constraints imposed by the traffic flow, which all can be utilized for better vehicle energy management. Optimal or near optimal decision-making based on this available information requires optimal control methods, whose fundamental theories were well studied in the past but are not directly applicable due to the complexity of real problems and uncertainty in the available preview information. This dissertation proposes the use of optimal control theories and tools including Pontryagin minimum principle, Dynamic Programming (DP) which is a numerical realization of Bellman\u27s principle of optimality, and Model Predictive Control (MPC) in the optimization-based control of hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and conventional vehicles based on preview of future route information. The dissertation includes three parts introduced as follows: First, the energy saving benefit in HEV energy management by previewing future terrain information and applying optimal control methods is explored. The potential gain in fuel economy is evaluated, if road grade information is integrated in energy management of hybrid vehicles. Real-world road geometry information is taken into account in power management decisions by using both Dynamic Programming (DP) and a standard Equivalent Consumption Minimization Strategy (ECMS), derived using Pontryagin minimum principle. Secondly, the contribution of different levels of preview to energy management of plug-in hybrid vehicles (PHEVs) is studied. The gains to fuel economy of plug-in hybrid vehicles with availability of velocity and terrain preview and knowledge of distance to the next charging station are investigated. Access to future driving information is classified into full, partial, or no future information and energy management strategies for real-time implementation with partial future preview are proposed. ECMS as well as Dynamic Programming (DP) is systematically utilized to handle the resulting optimal control problems with different levels of preview. We also study the benefit of future traffic flow information preview in improving the fuel economy of conventional vehicles by predictive control methods. According to the time-scale of the preview information and its importance to the driver, the energy optimization problem is decomposed into different levels. In the microscopic level, a model predictive controller as well as a car following model is employed for predictive adaptive cruise control by stochastically forecasting the driving behavior of the lead car. In the macroscopic level, we propose to incorporate the estimated macroscopic future traffic flow information and optimize the cost-to-go by utilizing a two-dimension Dynamic Programming (2D-DP). The algorithm yields the optimal trip velocity as the reference velocity for the driver or a low level controller to follow. Through the study, we show that energy use and emissions can be reduced considerably by using preview route information. The methodologies discussed in this dissertation provide an alternative mean for the automotive industry to develop more efficient and environmentally friendly vehicles by relying mostly on software and information and with minimal hardware investments

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Postprint (published version

Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

In this paper we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both power split and engine on/off decisions. The solution of a convex relaxation of the problem is used to initialize the optimization, which is necessarily nonconvex, and whilst only local convergence can be guaranteed, it is demonstrated that the algorithm will terminate with the optimal power split for the given engine switching sequence. The algorithm is compared in simulation against a charge-depleting/charge-sustaining (CDCS) strategy and dynamic programming (DP) using real world driver behaviour data, and it is demonstrated that the algorithm achieves 90\% of the fuel savings obtained using DP with a 3000-fold reduction in computational time

Time-optimal Control Strategies for Electric Race Cars with Different Transmission Technologies

This paper presents models and optimization methods to rapidly compute the achievable lap time of a race car equipped with a battery electric powertrain. Specifically, we first derive a quasi-convex model of the electric powertrain, including the battery, the electric machine, and two transmission technologies: a single-speed fixed gear and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the time-optimal control problem for a given driving path and solve it using an iterative convex optimization algorithm. Finally, we showcase our framework by comparing the performance achievable with a single-speed transmission and a CVT on the Le Mans track. Our results show that a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, and significantly outperform a fixed single-gear transmission.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and Propulsion Conferenc