Optimal control of a flywheel-based automotive kinetic energy recovery system

This thesis addresses the control issues surrounding flywheel-based Kinetic Energy Recovery Systems (KERS) for use in automotive vehicle applications. Particular emphasis is placed on optimal control of a KERS using a Continuously Variable Transmission (CVT) for volume car production, and a wholly simulation-based approach is adopted. Following consideration of the general control issues surrounding KERS operation, a simplified system model is adopted, and the scope for use of optimal control theory is explored. Both Pontryagin’s Maximum Principle, and Dynamic Programming methods are examined, and the need for numerical implementation established. With Dynamic Programming seen as the most likely route to practical implementation for realistic nonlinear models, the thesis explores several new strategies for numerical implementation of Dynamic Programming, capable of being applied to KERS control of varying degrees of complexity. The best form of numerical implementation identified (in terms of accuracy and efficiency) is then used to establish via simulation, the benefits of optimal KERS control in comparison with a more conventional non-optimal strategy, showing clear benefits of using optimal control

Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed

Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule-based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology

A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers

Razavian, R. S., Azad, N. L., & McPhee, J. (2013). A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers. International Journal of Electric and Hybrid Vehicles, 5(3), 177. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2013.057604We have developed a battery hardware-in-the-loop (HIL) setup, which can expedite the design and evaluation of power management controllers for hybrid electric vehicles (HEVs) in a novel cost- and time-effective manner. The battery dynamics have a significant effect on the HEV power management controller design; therefore, physical batteries are included in the simulation loop for greater simulation fidelity. We use Buckingham's Pi Theorem in the scaled-down battery HIL setup to reduce development and testing efforts, while maintaining the flexibility and fidelity of the control loop. In this paper, usefulness of the setup in parameter identification of a simple control-oriented battery model is shown. The model is then used in the power management controller design, and the real-time performance of the designed controller is tested with the same setup in a realistic control environment. Test results show that the designed controller can accurately capture the dynamics of the real system, from which the assumptions made in its design process can be confidently justified.Financial support for this research has been provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Toyota, and Maplesoft

development of an on-line energy management strategy for hybrid electric vehicle

Abstract The Hybrid Electric Vehicle (HEV) seems to be one of the most promising short-term solution to improve the sustainability on the transportation sector. As well-known, the numerical analyses can give a substantial contribute during the preliminary vehicle design. In this context, the development of the Energy Management Strategy (EMS) represents the most challenging task. In this paper, an on-line local optimization EMS for a parallel/series hybrid vehicle is proposed to minimize the CO2 emissions. The proposed EMS, implemented in a dynamic simulation platform, is compared to the well-assessed off-line Pontryagin's Minimum Principle (PMP). Firstly, the main differences regarding the energy management are highlighted in detail. Then, the EMSs are assessed in terms of CO2 emissions, putting into evidence that the proposed on-line strategy involves limited penalizations (3-4%) compared to the PMP target

Optimierung von Brennstoffzellen-Hybridfahrzeugen

The limited fossil fuel resources and the environmental concerns associated with burning those fossil fuels lie behind the increasing interest in hydrogen as a clean and sustainable alternative to fossil fuels, and in fuel cells as a clean converter of hydrogen into electrical energy especially in the transportation sector. Fuel cell hybrid vehicles (FCHVs) are characterized by the use of a fuel cell system (FCS) as the main power source and a battery, a supercapacitor or both as an energy storage system (ESS). Hybridizing the FCS with an ESS significantly improves the hydrogen economy, helps downsize the FCS, and resolves the issues related the long start-up time and slow dynamics of the FCS. The existence of multiple power sources in the powertrain gives rise to two important questions: How to coordinate the power contribution of the sources (i.e., power management strategy (PMS)), and how to size these sources in order to exploit the advantages of hybridization. The goal of this thesis is to develop a comprehensive framework for the optimization of PMS and size of FCHV powertrains. Depending on the type of ESS, three topologies are considered: fuel cell/ battery, fuel cell/ supercapacitor, and fuel cell/ battery/ supercapacitor. The PMS optimization is investigated on two levels; i.e., the vehicle level by simulation and the developed optimization algorithms are then validated on a small-scale test bench. When the driving cycle is known a priori, the off-line optimal PMS that globally minimizes the hydrogen consumption is calculated by two algorithms, namely, Dynamic Programming (DP) and Pontryagin’s Minimum Principle (PMP), and the two algorithms are compared. It has been found that PMP can be a superior approach for off-line optimization since it requires negligible computation resources without sacrificing the global optimality. The off-line optimal strategy is not real-time capable; hence, real-time strategies are designed and optimized while using the off-line optimal PMS as a benchmark. Special emphasize is put on the inclusion of multiple driving cycles, of different nature, in the optimization of the real-time PMS to increase its robustness. The sizing of the power sources of fuel cell/ battery and fuel cell/ supercapacitor hybrids considers hydrogen consumption and powertrain cost as two objectives and takes into account the drivability constraints such as top speed, gradeablity and acceleration time. The interesting designs (i.e., FCS size and ESS size), which represent the most efficient trade-off between the objectives, are then extracted and analyzed. The effect of battery aging on the optimal powertrain size is investigated by an Ampere-hour throughput model. It has been found that the battery aging leads to less efficient powertrain designs and the supercapacitor can become a more efficient option in comparison to batteries of poor lifetime.Die begrenzten fossilen Ressourcen und die Umweltsorgen, die mit der Verbrennung dieser fossilen Brennstoffe verbunden sind, stecken hinter dem steigenden Interesse am Wasserstoff als sauberer und nachhaltiger Alternative, und an Brennstoffzellen als sauberen Wandlern des Wasserstoffs in elektrische Energie, vor allem im Verkehrssektor. Ein Brennstoffzellen-Hybridfahrzeug (FCHV) verwendet ein Brennstoffzellensystem (FCS) als eine Hauptenergiequelle und eine Batterie, einen Superkondensator oder beide als Energiespeichersystem (ESS). Hybridisierung des FCS mit einem ESS verringert erheblich den Wasserstoffverbrauch, hilft das FCS zu verkleinern, und behebt das Problem der langen Anlaufzeit und der langsamen Dynamik des FCS. Die Existenz von mehreren Stromquellen im Antriebsstrang wirft zwei wichtige Fragen auf: Wie ist die Leistungsanforderung des Fahrzeugs zwischen den Quellen zu verteilen (d.h. Power-Management-Strategie (PMS)) und wie sind diese Quellen zu dimensionieren, um die Hybridisierung auszunutzen. Das Ziel dieser Arbeit ist es, einen umfassenden Rahmen für die Optimierung der PMS und Dimensionierung der Brennstoffzellen-basierten hybriden Antriebsstränge zu entwickeln. Abhängig von der Art des ESS werden drei Topologien berücksichtigt: Brennstoffzelle/ Batterie, Brennstoffzelle/ Superkondensator und Brennstoffzelle/ Batterie/ Superkondensator. Die PMS-Optimierung wird auf zwei Ebenen untersucht, und zwar die Fahrzeugebene durch Simulation und die Prüfstandsebene, worauf die entwickelten Optimierungsalgorithmen experimentell validiert werden. Wenn der Lastzyklus im Voraus bekannt ist, kann die offline optimale PMS, die den Wasserstoffverbrauch global minimiert, berechnet werden. Dazu werden die zwei Algorithmen, Dynamische Programmierung (DP) und Pontryagins Minimumprinzip (PMP), verglichen. Es wurde herausgefunden, dass das PMP ein überlegener Ansatz für die offline-Optimierung sein kann, da es viel weniger Rechenressourcen braucht, ohne die globale Optimalität zu opfern. Die offline optimale Strategie ist nicht echtzeitfähig, und deshalb werden Echtzeit-Strategien entworfen und optimiert, indem die offline optimale PMS als Maßstab verwendet wird. Beim Designen der echtzeitfähigen Strategien werden mehrere Fahrzyklen unterschiedlicher Natur beachtet, um die Robustheit der Strategien zu erhöhen. Die Dimensionierung der Stromquellen der Brennstoffzelle/ Batterie und Brennstoffzelle/ Superkondensator Hybriden betrachtet den Wasserstoffverbrauch und die Kosten des Antriebsstrangs als zwei Ziele. Es wird dabei die Fahrbarkeit, d.h. Höchstgeschwindigkeit, Steigfähigkeit und Beschleunigungszeit, berücksichtigt. Die interessanten Konfigurationen (FCS-Größe und ESS-Größe), die den effizientesten Kompromiss zwischen den Zielen darstellen, werden dann herausgefunden und analysiert. Die Wirkung der Batteriealterung auf die optimale Antriebsstrang-Größe wird durch ein Ampere-Stunden-Durchsatzmodell untersucht. Es wurde herausgefunden, dass die Batterie-Alterung weniger effiziente Antriebsstrang-Konfigurationen ergibt, und dass der Superkondensator eine effizientere Alternative zur Batterie sein kann, wenn er mit Batterien von schlechter Lebensdauer verglichen wird

An investigation on the effect of driver style and driving events on energy demand of a PHEV

Environmental concerns, security of fuel supply and CO2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV. However, how the necessary information can be obtained to use in EMS optimisation still remains a challenge. In this paper the effect of driver style and driving events like city and highway driving on plug in hybrid electric vehicle (PHEV) energy demand is studied. Using real world driving data from three drivers of very different driver style, a simulation has been exercised for a given route having city and highway driving. Driver style and driving events both affect vehicle energy demand. In both driving events considered, vehicle energy demand is different due to driver styles. The major part of city driving is reactive driving influenced by external factors and driver leading to variation in vehicle speed and hence energy demand. In free highway driving, the driver choice of cruise speed is the only factor affecting vehicle energy demand