SUMMARYHydraulic hybrid propulsion and energy storage components demonstrate characteristics that are very different from their electric counterparts, thus requiring unique control strategies. This paper presents a methodology for developing a power management strategy tailored specifically to a parallel Hydraulic Hybrid Vehicle (HHV) configured for a medium-size delivery truck.The Hydraulic H ybrid Vehicle is modelled in the MATLAB/SIMULINK environment to facilitate system integration and control studies . A Dynamic Programming (DP) algorithm is used to obtain optimal control actions for gear shifting and power splitting bet ween the engine and the hydraulic motor over a representative urban driving schedule . Features of optimal trajectories are then studied to derive i mplementable rules . System behaviour demonstrates that the new control strategy takes advantage of high power density and efficiency characteristics of hydraulic components, and minimizes disadvantages of low energy density, to achieve enhanced overall efficiency . Simulation results indicate that the potential for fuel economy improvement of medium trucks with hydraulic hybrid propulsion can be as high as 48 %.

SUMMARYThe design procedure for an adaptive power management control strategy, based on a driving pattern recognition algorithm is proposed. The design goal of the control strategy is to minimize fuel consumption and engine-out NOx and PM emissions on a set of diversified driving schedules. Six representative driving patterns (RDP) are designed to represent different driving scenarios. For each RDP, the Dynamic Programming (DP) technique is used to find the global optimal control actions. Implementable, sub-optimal control algorithms are then extracted by analyzing the behavior of the DP control actions. A driving pattern recognition (DPR) algorithm is subsequently developed and used to classify the current driving pattern into one of the RDPs; thus, the most appropriate control algorithm is selected adaptively. This "multi-mode" control scheme was tested on several driving cycles and was found to work satisfactorily.

Abstract:The main contributions of this paper are the development of a forward-looking hybrid vehicle simulation tool, and its application to the design of a power management control algorithm. The hybrid electric vehicle simulation tool (HE-VESIM) was developed at the Automotive Research Center of the University of Michigan to study the potential fuel economy and emission benefits of the parallel hybrid propulsion system for a medium truck. The fundamental architecture of the feed-forward simulation tool and the dynamic equations of its sub-system modules are first described. A power management control algorithm is then designed and evaluated, which is based on mimicking the behaviour of a dynamic-programming optimisation scheme. Simulation results over an urban driving cycle demonstrate that the hybrid control algorithm is able to improve vehicle fuel economy significantly, compared with the original vehicle, powered only by a diesel engine.

The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and is based on the dynamic programming technique. A vehicle model is first developed, and the optimal control actions to maximize fuel economy are then obtained by the dynamic programming method. A near-optimal control strategy is subsequently extracted and implemented using a rapid-prototyping control development system, which provides a convenient environment to adjust the control algorithms and accommodate various I/O configurations. Dynamometer-testing results confirm that the proposed algorithm helps the prototype hybrid truck to achieve a 45% fuel economy improvement on the benchmark (non-hybrid) vehicle. It also compares favorably to a conventional rule-based control method, which only achieves a 31% fuel economy improvement on the same hybrid vehicle.

System-level modeling and control strategy development for a fuel cell hybrid vehicle (FCHV) are presented in this paper. A reduced-order fuel cell model is created to accurately predict the fuel cell system efficiency while retaining dynamic effects of important variables. The fuel cell system model is then integrated with a DC/DC converter, a Li-ion battery, an electric drive, and tire/vehicle dynamics to form an FCHV. In order to optimize the power management strategy of the FCHV, we develop a stochastic design approach based on the Markov chain modeling and stochastic dynamic programming (SDP). The driver demand is modeled as a Markov process to represent the future uncertainty under diverse driving conditions. The infinite-horizon SDP solution generates a stationary state-feedback control policy to achieve optimal power management between the fuel cell system and battery. Simulation results over different driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach.