Low-Cost Pathway to Ultra Efficient City Car: Series Hydraulic Hybrid System with Optimized Supervisory Control

A series hydraulic hybrid concept (SHHV) has been explored as a potential pathway to an ultra-efficient city vehicle. Intended markets would be congested metropolitan areas, particularly in developing countries. The target fuel economy was ~100 mpg or 2.4 l/100km in city driving. Such an ambitious target requires multiple measures, i.e. low mass, favorable aerodynamics and ultra-efficient powertrain. The series hydraulic hybrid powertrain has been designed and analyzed for the selected light and aerodynamic platform with the expectation that (i) series configuration will maximize opportunities for regeneration and optimization of engine operation, (ii) inherent high power density of hydraulic propulsion and storage components will yield small, low-cost components, and (iii) high efficiency and high power limits for accumulator charging/discharging will enable very effective regeneration. The simulation study focused on the SHHV supervisory control development, to address the challenge of the low storage capacity of the accumulator. Two approaches were pursued, i.e. the thermostatic SOC control, and Stochastic Dynamic Programming for horizon optimization. The stochastic dynamic programming was setup using a set of naturalistic driving schedules, recorded in normal traffic. The analysis included additional degree of freedom, as the engine power demand was split into two variables, namely engine torque and speed. The results represent a significant departure from the conventional wisdom of operating the engine near its “sweet spot” and indicate what is preferred from the system stand-point. Predicted fuel economy over the EPA city schedule is ~93 mpg with engine idling, and ~110 mpg with engine shut-downs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89873/1/SAE_2009-24-0065_draft.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/89873/3/SAE 2009-24-0065.pd

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

Simultaneous Optimization Of Supervisory Control And Gear Shift Logic For A Parallel Hydraulic Hybrid Refuse Truck Using Stochastic Dynamic Programming

The power management controller of a hybrid vehicle orchestrates the operation of onboard energy sources, namely engine and auxiliary power source with the goal of maximizing performance objectives such as the fuel economy. The paper focuses on optimization of the power management strategy of the refuse truck with parallel hydraulic hybrid powertrain. The high power density of hydraulic components and high charging/discharging efficiency of accumulator with no power constraint make hydraulic hybrid an excellent choice for heavy-duty stop and go application. Two power management strategies for a parallel hydraulic hybrid refuse truck are compared; heuristic and stochastic dynamic programming based optimal controller. For designing a SDP based controller, an infinite horizon problem is setup with power demand from driver modeled as random Markov process. The objective is to maximize system level efficiency by optimizing (i) the power split between engine and hydraulic propulsion unit, and (ii) gear shift schedule. This combines the optimization of powertrain parameters with power management design.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89878/1/draft_01.pd

Real-Time Transient Soot and NOx Virtual Sensors for Diesel Engine using Neuro-Fuzzy Model Tree and Orthogonal Least Squares

Diesel engine combustion and emission formation is highly nonlinear and thus creates a challenge related to engine diagnostics and engine control with emission feedback. This paper presents a novel methodology to address the challenge and develop virtual sensing models for engine exhaust emission. These models are capable of predicting transient emissions accurately and are computationally efficient for control and optimization studies. The emission models developed in this paper belong to the family of hierarchical models, namely “neuro-fuzzy model tree”. The approach is based on divide-and-conquer strategy i.e. to divide a complex problem into multiple simpler subproblems, which can then be identified using simpler class of models. Advanced experimental setup incorporating a medium duty diesel engine is used to generate training data. Fast emission analyzers for soot and NOX provide instantaneous engine-out emissions. Finally, the Engine-In-the-Loop is used to validate the models for predicting transient particulate mass and NOX.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89877/1/draft_01.pd

Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions.

Sequential decision problems under uncertainty are encountered in various fields such as optimal control and operations research. In this dissertation, Neuro-Dynamic Programming (NDP) and Reinforcement Learning (RL) are applied to address policy optimization problems with multiple objectives and large design state space. Dynamic Programming (DP) is well suited for determining an optimal solution for constrained nonlinear model based systems. However, DP suffers from curse of dimensionality i.e. computational effort grows exponentially with state space. The new algorithms address this problem and enable practical application of DP to a much broader range of problems. The other contribution is to design fast and computationally efficient transient emission models. The power management problem for a hybrid vehicle can be formulated as an infinite time horizon stochastic sequential decision-making problem. In the past, policy optimization has been applied successfully to design optimal supervisory controller for best fuel economy. Static emissions have been considered too but engine research has shown that transient operation can have significant impact on real-world emissions. Modeling transient emissions results in addition of more states. Therefore, the problem with multiple objectives i.e. minimize fuel consumption and transient particulate and NOX emissions, becomes computationally intractable by DP. This research captures the insight with models and brings it into the supervisory controller design. A self-learning supervisory controller is designed based on the principles of NDP and RL. The controller starts “naïve” i.e. with no knowledge to control the onboard power but learns to do so in an optimal manner after interacting with the system. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. Virtual sensors for predicting real-time transient particulate and NOX emissions are developed using neuro-fuzzy modeling technique, which utilizes a divide-and-conquer strategy. The highly nonlinear engine operating space is partitioned into smaller subspaces and a separate local model is trained to for each subspace. Finally, the supervisory controller along with virtual emission sensors is implemented and evaluated using the Engine-In-the-Loop (EIL) setup. EIL is a unique facility to systematically evaluate control methodologies through concurrent running of real engine and a virtual hybrid powertrain.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89829/1/rajit_1.pd

Optimal Energy Management for a Hybrid Vehicle Using Neuro-Dynamic Programming to Consider Transient Engine Operation

This paper proposes a self-learning approach to develop optimal power management with multiple objectives, e.g. to minimize fuel consumption and transient engine-out NOx and particulate matter emission for a series hydraulic hybrid vehicle. Addressing multiple objectives is particularly relevant in the case of a diesel powered hydraulic hybrid since it has been shown that managing engine transients can significantly reduce real-world emissions. The problem is formulated as an infinite time horizon stochastic sequential decision making/markovian problem. The problem is computationally intractable by conventional Dynamic programming due to large number of states and complex modeling issues. Therefore, the paper proposes an online self-learning neural controller based on the fundamental principles of Neuro-Dynamic Programming (NDP) and reinforcement learning. The controller learns from its interactions with the environment and improves its performance over time. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. The control law is a stationary full state feedback based on 5 states and can be directly implemented. The controller performance is then evaluated in the Engine-in-the-Loop (EIL) facility.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89876/1/draft_01.pd

Hydraulic Hybrid Powertrain-In-the-Loop Integration for Analyzing Real-World Fuel Economy and Emissions Improvements

The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. The configuration of the hydraulic-hybrid system and details of the hydraulic circuit developed for the H-PIL integration are presented. Next, software and hardware interfaces between the real components and virtual systems are developed, and special attention is given to linking component-level controllers and system-level supervisory control. The H-PIL setup allows imposing realistic dynamic loads on hydraulic pump/motors and accumulator based on vehicle driving schedule. Application of fast analyzers allows characterization of the impact of dynamic interactions in the propulsion system on engine-out emissions. Therefore, the H-PIL facility allows optimization of the hybrid system for both high-efficiency and low emissions. The impetus is provided by previous work showing that more than half of the soot emissions from a conventional diesel powertrain over the urban driving schedule can be attributed to transients. The setup includes a 6.4L V-8 International diesel engine, highly dynamic dynamometer, Radial piston pump/motors supplied by Bosch-Rexroth and dSPACE real-time environment with in-house developed simulation of the virtual vehicle.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89880/1/draft_01.pd

Simulation Study of Advanced Variable Displacement Engine Coupled to Power-Split Hydraulic Hybrid Powertrain

The simulation based investigation of the variable displacement engine is motivated by a desire to enable unthrottled operation at part load, and hence eliminate pumping losses. The mechanism modeled in this work is derived from a Hefley engine concept. Other salient features of the proposed engine are turbocharging and cylinder deactivation. The cylinder deactivation combined with variable displacement further expands the range of unthrottled operation, whereas turbocharging increases the power density of the engine and allows downsizing without the loss of performance. Although the proposed variable displacement turbocharged engine (VDTCE) concept enables operations in a very wide range, running near idle is impractical. Therefore, the VDTCE is integrated with a hybrid powertrain to mitigate issues with engine transients and mode transitions. The engine model is developed in AMESim using physics based models, such as thermodynamic cycle simulation, filling and emptying of manifolds, and turbulent flame entrainment combustion. A predictive model of the power-split hydraulic hybrid driveline is created in SIMULINK, thus facilitating integration with the engine. The integrated simulation tool is utilized to address design and control issues, before determining the fuel economy potential of the powertrain comprising a VDTCE engine and a hydraulic hybrid driveline.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89879/1/draft_01.pd