SYSTEM MODELING AND POWER MANAGEMENT STRATEGY FOR A SERIES HYDRAULIC HYBRID VEHICLE

A hydraulic hybrid vehicle draws propulsion power from an internal combustion engine as its prime mover and a gas-charged hydro-pneumatic accumulator as its energy buffer. The accumulator serves the purposes of storing regenerated braking energy and supplementing engine power as determined by an on-board power management strategy. In the configuration known as a series hydraulic hybrid powertrain, the engine is mechanically decoupled from the vehicle\u27s wheels thereby offering excellent opportunities for maximizing energy efficiency and reducing pollutant emissions. This thesis dealt with the development of a causally interconnected, non-linear, dynamic model of a series hydraulic hybrid powertrain featuring independently controllable wheel-end drives. Using the model so developed, the work investigated the potentials of three proposed power management strategies on the fuel/energy use of a test vehicle. The strategies studied included: a real-time implementable rule-based strategy, an on-line solvable instantaneous consumption minimization strategy, and a non-causal trip/globally optimal power management strategy based on dynamic programming. The results indicated that, when properly designed, all three power management strategies can help realize the fuel economy benefits of the proposed hydraulic hybrid drive system. Over a standard city drive cycle, the rule-based power management strategy was shown to provide a fuel economy improvement of more than 30% with four-motor drive over the conventional drive system. The trip/globally optimal strategy obtained via dynamic programming gave an average of over 50% higher fuel economy improvement with four-motor drive. The instantaneous consumption minimization strategy, which is adopted to overcome the non-causality of dynamic programming and the lack of rigorous optimality of the rule-based strategy, gave fuel economy improvements that generally fell between the other two strategies. Results are also included from the analysis of the effects of accumulator size and two-motor vs. four motor drive options along with the choice of the power management strategy

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

Advanced electric vehicle components for long-distance daily trips

This paper introduces a holistic engineering approach for the design of an electric sport utility vehicle focused on the reliable capability of long-distance daily trips. This approach is targeting integration of advanced powertrain and chassis components to achieve energy-efficient driving dynamics through manifold contribution of their improved functions. The powertrain layout of the electric vehicle under discussion is designed for an e-traction axle system including in-wheel motors and the dual inverter. The main elements of the chassis layout are the electro-magnetic suspension and the hybrid brake-by-wire system with electro-hydraulic actuators on the front axle and the electro-mechanical actuators on the rear axle. All the listed powertrain and chassis components are united under an integrated vehicle dynamics and energy management control strategy that is also outlined in the paper. The study is illustrated with the experimental results confirming the achieved high performance on the electric vehicle systems level

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

Development of a predictive thermal management function for Plug-in Hybrid Electric Vehicles

The present thesis is focused on the development of a predictive control strategy oriented to battery thermal management for plug-in hybrid electric vehicles (PHEVs). The basic principle of the strategy is to reduce as much as possible battery energy usage related to power request from the respective cooling circuit actuators. At this end, a thermo-hydraulic model of the in-vehicle battery cooling circuit has been developed in AMESim environment. Then, it has been implemented in an already existing Simulink vehicle model, which includes components analytical models and control strategies. The predictive aspect of the novel strategy is related to the evaluation of battery temperature over the electronic horizon on the base of input signals such as vehicle speed and road slope profile. As a consequence of temperature prediction, the developed strategy is able to establish in an energy-efficient way if cooling power is either required or not. Results highlight the advantages of applying the predictive strategy instead of a rule-based one, which is on-board implemented in each vehicle. It is shown that major energetic benefits, related to the extension of the all-electric range and the reduction of fuel consumption, take place at middle environmental temperatures, at which battery cooling power request can seriously make the difference on its drain rate. Therefore, project goal has been reached and the results can be considered an interesting starting point for further development and enhancing of predictive control strategies

Design and Analysis of Hydraulic Hybrid Passenger Vehicles

University of Minnesota Ph.D. dissertation. September 2015. Major: Mechanical Engineering. Advisors: Perry Li, Thomas Chase. 1 computer file (PDF); x, 305 pages.The research described in this dissertation focuses on the development of computationally efficient design methodology to optimize the hydraulic hybrid power-split transmission for fuel efficiency, acceleration performance and robustness against powertrain uncertainties. This research also involve experimental implementation of a three-level hierarchical control approach on two test beds, requiring powertrain control design and fine-tuning. Hybrid powertrains have the potential to benefit the fuel efficiency of highway and off-highway vehicles. Hydraulic hybrid has high power density. Hydraulic power-split architecture is chosen in this study for its flexibility in operation and combined advantage of series and parallel architecture. An approach for optimizing the configuration and sizing of a hydraulic hybrid power-split transmission is proposed. Instead of considering each mechanical configuration consisting of combinations of gear ratios, a generalized kinematic relation is used to avoid redundant computation. The Lagrange multiplier method for computing the optimal energy management control is shown to be 450 times more computationally efficient for use in transmission design iterations. To exploit the benefit of high power density of hydraulics, a classical multi-objective solver is utilized to incorporate the acceleration performance criteria into the transmission design optimization. By considering worst-case uncertainty, the transmission design is optimized to be robust against powertrain uncertainties and insensitive to operating condition variations, and yet fuel efficient. The Generation I and II vehicles are experimental platforms built to implement controls and to validate the fuel efficiency gain for power-split transmission. The powertrain for the platforms are modeled to predict the potential fuel efficiency improvement by different energy management strategies. Results show maximum of 74\% fuel efficiency gain by optimizing engine management from CVT to full optimal hybrid operation. The three-level control strategy is implemented on the Generation I vehicle. This control strategy segregates the tasks of the drive-train into three layers that respectively 1) manages the accumulator energy storage (high level); 2) performs vehicle level optimization (mid-level); and 3) attains the desired vehicle operating condition (low level). Results validated the modularity and effectiveness of this control structure

Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

An investigation into hybrid power trains for vehicles with regenerative braking

Imperial Users onl

Analysis of a Parallel Hybrid Electric Tractor for Agricultural Applications

The field of Non-Road Mobile Machineries (NRMM) is now more than ever considering the adoption of electric systems to reduce the amount of pollutant emissions per unit of work. However, the intensity and complexity of the tasks performed by a working machine during its life is an obstacle to the widespread adoption of electric systems. Specific design solutions are required to properly split the power output of the hybrid powertrain among the different loads (wheel, power take off, hydraulic tools, etc.). In this work, a performance analysis between a traditional agricultural tractor and a proposed hybrid electric architecture of the same vehicle is shown. The comparison was performed on a set of tasks characterized on a real orchard tractor which were used to build the input signals of two different numerical models: one for the traditional diesel architecture and the other for the hybrid electric solution. The two models were tested with the same operating tasks to have a one to one comparison of the two architectures. Peak power capabilities of the hybrid solution and performance of the Load Observer energy management strategy were investigated to validate the feasibility of the proposed solution