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Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action.

The duration over which a three way catalyst (TWC) maintains proper functionality during lambda excursions is critically impacted by aging, which affects its oxygen storage capacity (OSC). As such, emissions control strategies, which strive to maintain post TWC air-to-fuel ratios at the stoichiometric value, will benefit from an accurate estimation of TWC age. To this end, this investigation examines a method of TWC age estimation suitable for real-world transient operation. Experimental results are harvested from an instrumented test vehicle equipped with a two-brick TWC during operation on a chassis dynamometer. Four differently aged TWCs are instrumented with wideband and switch-type Lambda sensors upstream (Pre TWC location), and downstream (Mid location) of first catalyst brick.