  This paper presents model-based and model-free learning methods for economic
and ecological adaptive cruise control (Eco-ACC) of connected and autonomous
electric vehicles. For model-based optimal control of Eco-ACC, we considered
longitudinal vehicle dynamics and a quasi-steady-state powertrain model
including the physical limits of a commercial electric vehicle. We used
adaptive dynamic programming (ADP), in which the value function was trained
using data obtained from IPG CarMaker simulations. For real-time
implementation, forward multi-step look-ahead prediction and optimization were
executed in a receding horizon scheme to maximize the energy efficiency of the
electric machine while avoiding rear-end collisions and satisfying the
powertrain, speed, and distance-gap constraints. For model-free optimal control
of Eco-ACC, we applied two reinforcement learning methods, Deep Q-Network (DQN)
and Deep Deterministic Policy Gradient (DDPG), in which deep neural networks
were trained in IPG CarMaker simulations. For performance demonstrations, the
HWFET, US06, and WLTP Class 3b driving cycles were used to simulate the front
vehicle, and the energy consumptions of the host vehicle and front vehicle were
compared. In high-fidelity IPG CarMaker simulations, the proposed
learning-based Eco-ACC methods demonstrated approximately 3-5% and 10-14%
efficiency improvements in highway and city-highway driving scenarios,
respectively, compared with the front vehicle. A video of the CarMaker
simulation is available at https://youtu.be/DIXzJxMVig8.
