Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management.

This work presents three computational methods for real time energy management in a hybrid hydraulic vehicle (HHV) when driver behavior and vehicle route are not known in advance. These methods, implemented in a receding horizon control (aka model predictive control) framework, are rather general and can be applied to systems with nonlinear dynamics subject to a Markov disturbance. State and input constraints are considered in each method. A mechanism based on the steady state distribution of the underlying Markov chain is developed for planning beyond a finite horizon in the HHV energy management problem. Road elevation information is forecasted along the horizon and then merged with the statistical model of driver behavior to increase accuracy of the horizon optimization. The characteristics of each strategy are compared and the benefit of learning driver behavior is analyzed through simulation on three drive cycles, including one real world drive cycle. A simulation is designed to explicitly demonstrate the benefit of adapting the Markov chain to real time driver behavior. Experimental results demonstrate the real time potential of the primary algorithm when implemented on a processor with limited computational resources

Supervisory Control Optimization for a Series Hybrid Electric Vehicle with Consideration of Battery Thermal Management and Aging

This dissertation integrates battery thermal management and aging into the supervisory control optimization for a heavy-duty series hybrid electric vehicle (HEV). The framework for multi-objective optimization relies on novel implementation of the Dynamic Programing algorithm, and predictive models of critical phenomena. Electrochemistry based battery aging model is integrated into the framework to assesses the battery aging rate by considering instantaneous lithium ion (Li+) surface concentration rather than average concentration. This creates a large state-action space. Therefore, the computational effort required to solve a Deterministic or Stochastic Dynamic Programming becomes prohibitively intense, and a neuro-dynamic programming approach is proposed to remove the ‘curse of dimensionality’ in classical dynamic programming. First, unified simulation framework is developed for in-depth studies of series HEV system. The integration of a refrigerant system model enables prediction of energy use for cooling the battery pack. Side reaction, electrolyte decomposition, is considered as the main aging mechanism of LiFePO4/Graphite battery, and an electrochemical model is integrated to predict side reaction rate and the resulting fading of capacity and power. An approximate analytical solution is used to solve the partial difference equations (PDEs) for Li+ diffusion. Comparing with finite difference method, it largely reduces the number of states with only a slight penalty on prediction accuracy. This improves computational efficiency, and enables inclusion of the electrochemistry based aging model in the power management optimization framework. Next, a stochastic dynamic programming (SDP) approach is applied to the optimization of supervisory control. Auxiliary cooling power is included in addition to vehicle propulsion. Two objectives, fuel economy and battery life, are optimized by weighted sum method. To reduce the computation load, a simplified battery aging model coupled with equivalent circuit model is used in SDP optimization; Li+ diffusion dynamics are disregarded, and surface concentration is represented by the average concentration. This reduces the system state number to four with two control inputs. A real-time implementable strategy is generated and embedded into the supervisory controller. The result shows that SDP strategy can improve fuel economy and battery life simultaneously, comparing with Thermostatic SOC strategy. Further, the tradeoff between fuel consumption and active Li+ loss is studied under different battery temperature. Finally, the accuracy of battery aging model for optimization is improved by adding Li+ diffusion dynamics. This increases the number of states and brings challenges to classical dynamic programming algorithms. Hence, a neuro-dynamic programming (NDP) approach is proposed for the problem with large state-action space. It combines the idea of functional approximation and temporal difference learning with dynamic programming; in that case the computation load increases linearly with the number of parameters in the approximate function, rather than exponentially with state space. The result shows that ability of NDP to solve the complex control optimization problem reliably and efficiently. The battery-aging conscientious strategy generated by NDP optimization framework further improves battery life by 3.8% without penalty on fuel economy, compared to SDP strategy. Improvements of battery life compared to the heuristic strategy are much larger, on the order of 65%. This leads to progressively larger fuel economy gains over time

Self-Learning Neural controller for Hybrid Power Management using Neuro-Dynamic Programming

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the cost-to-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89874/1/draft_01.pd

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Advanced Torque Control Strategy for the Maha Hydraulic Hybrid Passenger Vehicle.

An increase in the number of vehicles per capita coupled with stricter emission regulations have made the development of newer and better hybrid vehicle architectures indispensable. Although electric hybrids have more visibility and are now commercially available, hydraulic hybrids, with their higher power densities and cheaper components have been rigorously explored as the alternative. The most commonly used architecture is the series hybrid, which requires a power conversion from the primary source (engine) to the secondary domain. A positive displacement machine (pump) converts the rotational power of the engine into hydraulic power and a second positive displacement machine (motor) converts the hydraulic power back into rotational power to drive the axle or wheel. Having at least one variable displacement unit enables the system of the pump and the motor to form a continuously variable transmission. A series hybrid also includes a secondary power storage device, which in most cases is a high-pressure hydro-pneumatic accumulator. During braking, power flows from the wheels, which drive the second positive displacement machine into the high-pressure accumulator and during acceleration, the power flow is reversed, i.e. power from the high-pressure accumulator is used as an input for the second positive displacement machine which will run in motoring mode and drive the axle or wheel. A mode-switching hydraulic hybrid, which is a combination of a hydrostatic transmission and a series hybrid was recently developed at the Maha Fluid Power Research Center. This thesis focuses on the development of a new torque-based controller for the mode-switching hydraulic hybrid prototype. The aim of this work is to use a uniform control strategy across all vehicle modes instead of multiple controllers for multiple modes. With that in mind, an entirely new system model is developed. This torque-based control strategy, along-with a supervisory controller decides on the usage of the high-pressure accumulator, thereby switching the vehicle from non-hybrid to hybrid mode. A separate engine speed controller is designed to control the engine throttle based on the measured engine speed and a piecewise constant reference engine speed. The model is simulated using standard drive cycles demonstrating the different vehicle modes of operation and the controller action. The architecture of the existing prototype vehicle is modified to implement the new controller and also to prevent leakages when the vehicle is not in use. The data acquisition system is modified to incorporate new installed components. Lastly, baseline measurements taken with the prototype vehicle are compared with the simulations. This improved control strategy allows the vehicle to operate in higher powertrain efficiencies and the uniform nature of the controller results in a better “driver-feel”

Improving System Design and Power Management for Hybrid Hydraulic Vehicles Minimizing Fuel Consumption

Im Fokus dieser Arbeit steht die optimale Gestaltung und Steuerung von hybridhydraulischen Fahrzeugen, um die gewünschten Eigenschaften des Systems, nämlich Kraftstoffverbrauch und Fahrbarkeit zu verbessern. Trotz unterschiedlich optimaler und suboptimaler Power-Management-Strategien sind typische Probleme im Umgang mit der Echtzeit-Anwendbarkeit von Power-Management-Strategien, speziell Rechenlast und Lastzyklus-Anforderungen noch diskutierte Themen in diesem Zusammenhang. Basierend auf den Modellen der hydraulischen Komponenten und Teilsysteme werden typische hydraulische Hybridfahrzeugtopologien entwickelt, verifiziert und mit Simulationsergebnissen aus technischer Software für die Modellierung der Hydrauliksysteme verglichen. Die parametrischen Modelle können für die Umsetzung des typischen optimalen Power-Management Strategien verwendet werden. Der Kern der Arbeit ist die Entwicklung, Anwendung, Optimierung und Auswertung von Druckregelstrategien, Power-Management-Strategien, sowie die Optimierung des Systemdesigns. Obwohl regelbasierte Druckregelstrategien suboptimale Ansätze darstellen, werden Kraftstoffverbrauch und Fahrverhalten in Form von Referenz Geschwindigkeits-Trackings als wichtigste Kriterien für eine angemessene Bewertung und den Vergleich von optimierten Power-Management-Strategien im Rahmen dieser Arbeit berücksichtigt. Um eine Online-Optimale Power-Management-Strategie zu entwickeln, sind drei Arten von Multi-objective Multi-parametric optimalen Steuerungsprobleme entwickelt und im System angewendet worden. Hierbei werden verschiedene Optimierungsalgorithmen wie Dynamic Programming (DP), Non-dominated Sorting Genetic Algorithm II (NSGA II) und Model Predictive Control (MPC) angewendet. Der Hauptgrund für die Entwicklung von DP-basierten Leistungsverwaltungen ist die Entwicklung eines Off-line Power Management, um die Leistung von anderen entwickelten Algorithmen zu bewerten. Anschließend wird eine Instantaneous Optimized Power Management (IOPM) auf der Grundlage quasi-statischer Modelle des Systems entwickelt, welche auf dem momentanen Leistungsbedarf in jedem Zeitschritt basiert. Die entwickelten Power Management Strategien verbessern erheblich die gewünschten Systemeigenschaften, nämlich Effizienz und Fahrbarkeit Regelbasierte Druckregelstrategien sowie IOPM zeigen eine vergleichbare Performance. Basierend auf den entwickelten Off-line Power Management Strategien wird ein globaler Optimierungsansatz für die gleichzeitige Optimierung von Design-und Steuerungsparametern in diesem Beitrag entwickelt. Mit diesem Ansatz kann eine optimale Systemauslegung und deren Regelparameter an die angegebene Topologie für eine beliebige Fahrzeugklasse erreicht werden.The focus of this thesis is the optimal design and the optimal control of Hybrid Hydraulic Vehicles (HHV) to improve system desired characteristics, fuel consumption, and driveability. Despite the different optimal and sub-optimal power management strategies, typical challenging problems dealing with real-time applicability of the power management strategies particularly computational load and load cycle requirements are still discussing topics in this context. Based on the hydraulic components models as well as subsystems, typical HHV topologies are developed and verified compared to simulation results obtained from a technical software for the modeling of hydraulic systems. The parametric models can be used for the implementation of different optimal power management strategies. The core of the thesis is development, application, optimization, and evaluation of pressure control strategies, power management strategies, as well as the optimization of system design. Whereas rule-based pressure control strategies are sub-optimal approaches, fuel consumption and driveability in the form of reference velocity tracking are considered as the main criteria for a reasonable identical evaluation and comparison of optimized power management strategies developed within this thesis. In order to develop an on-line applicable optimal power management strategy, three types of multi-objective multi-parametric optimal control problems are developed and applied to the system. Hereby different optimization algorithms like Dynamic Programming (DP), Non-dominated Sorting Genetic Algorithm II (NSGA II), and Model Predictive Control (MPC) are applied. The main reason for the development of DP-based power management is the development of an off-line control trajectory in order to evaluate the performance of other developed algorithms. Subsequently, an Instantaneous Optimized Power Management (IOPM) based on the quasi-static model of the system is developed. This optimal power management operates based on instantaneous information about power demand without the consideration of time variation effects. The developed power management strategies significantly improve the system desired characteristics, namely efficiency and driveability. However, rulebased pressure control strategies as well as IOPM have comparable performances. Based on the developed off-line power management strategies, a global optimization approach for the simultaneous optimization of design and control parameters is developed within this contribution. Using this approach, optimal system design and control parameters related to the given topology for an arbitrary vehicle class can be achieved

Influence of Architecture Design on the Performance and Fuel Efficiency of Hydraulic Hybrid Transmissions

Hydraulic hybrids are a proven and effective alternative to electric hybrids for increasing the fuel efficiency of on-road vehicles. To further the state-of-the-art this work investigates how architecture design influences the performance, fuel efficiency, and controllability of hydraulic hybrid transmissions. To that end a novel neural network based power management controller was proposed and investigated for conventional hydraulic hybrids. This control scheme trained a neural network to generalize the globally optimal, though non-implementable, state trajectories generated by dynamic programming. Once trained the neural network was used for online prediction of a transmission’s optimal state trajectory during untrained cycles forming the basis of an implementable controller. During hardware-in-the-loop (HIL) testing the proposed control strategy improved fuel efficiency by up to 25.5% when compared with baseline approaches. To further improve performance and fuel efficiency a novel transmission architecture termed a Blended Hydraulic Hybrid was proposed and investigated. This novel architecture improves on existing hydraulic hybrids by partially decoupling power transmission from energy storage while simultaneously providing means to recouple the systems when advantageous. Optimal control studies showed the proposed architecture improved fuel efficiency over both baseline mechanical and conventional hydraulic hybrid transmissions. Effective system level and supervisory control schemes were also proposed for the blended hybrid. In order to investigate the concept’s feasibility a blended hybrid transmission was constructed and successfully tested on a HIL transmission dynamometer. Finally to investigate controllability and driver perception an SUV was retrofitted with a blended hybrid transmission. Successful on-road vehicle testing showcased the potential of this novel hybrid architecture as a viable alternative to more conventional electric hybrids in the transportation sector