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Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programing (SDP) controller with minimax dynamic programing (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a tradeoff of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions. [ABSTRACT FROM AUTHOR]

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy. [ABSTRACT FROM AUTHOR]

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%-9% being realizable. [ABSTRACT FROM AUTHOR]