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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 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.

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.

The majority of a refuse truck collection cycle consists of frequent Stop and Go events while moving from one household to another. The nature of this driving mission creates the opportunity to reduce fuel consumption by capturing and re-using the kinetic energy normally wasted during braking. This paper includes the evaluation of the brake energy available for regeneration from the conventional drivetrain; the description of the impact of the vehicle variable mass and auxiliary loads; a model validation over a real-world duty cycle; and the potential for an increase in fuel efficiency through hybridization of the drivetrain. The Hydraulic Hybrid (HH) technology is selected since it has a large power density.