Energy Efficient Solution for Connected Electric Vehicle and Battery Health Management Using Eco-Driving Under Uncertain Environmental Conditions

Abstract

Adopting energy-efficient driving practices can harness the full benefits of EVs. This work uses a multi-objective optimization strategy to perform eco-driving to reduce the energy consumption of EVs and to prolong the health of batteries. The problem jointly considers constraints of conflicting nature; such as traffic signals, preceding vehicles, limitations on speed and acceleration, checks on input torque and its rate of change and bounds on battery's SoC and charging/discharging rates. This research also explores how adhering strictly to one constraint may compromise other constraints. A comprehensive control strategy using MPC is adopted to formulate eco-driving as nonlinear programming and to achieve a realistic and optimal solution. The proposed strategy has successfully achieved eco-driving along with satisfying all the conflicting constraints in uncertain environmental conditions. Furthermore, results are compared with PMP to validate the optimal solution. SoH analysis indicates that the inclusion of battery-related constraints improves the battery's health. Finally, Lyapunov stability analysis is conducted to check the systems' stability with parametric uncertainty.