Ecological Advanced Driver Assistance System for Optimal Energy Management in Electric Vehicles

in IEEE Intelligent Transportation Systems Magazine (2018)

Battery Electric Vehicles have a high potential in modern transportation, however, they are facing limited cruising range. The driving style, the road geometries including slopes, curves, the static and ... [more ▼]

Battery Electric Vehicles have a high potential in modern transportation, however, they are facing limited cruising range. The driving style, the road geometries including slopes, curves, the static and dynamic traffic conditions such as speed limits and preceding vehicles have their share of energy consumption in the host electric vehicle. Optimal energy management based on a semi-autonomous ecological advanced driver assistance system can improve the longitudinal velocity regulation in a safe and energy-efficient driving strategy. The main contribution of this paper is the design of a real-time risk-sensitive nonlinear model predictive controller to plan the online cost-effective cruising velocity in a stochastic traffic environment. The basic idea is to measure the relevant states of the electric vehicle at runtime, and account for the road slopes, the upcoming curves, and the speed limit zones, as well as uncertainty in the preceding vehicle behavior to determine the energy-efficient velocity profile. Closed-loop Entropic Value-at-Risk as a coherent risk measure is introduced to quantify the risk involved in the system constraints violation. The obtained simulation and field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of safe and energy-efficient states regulation and constraints satisfaction. [less ▲]

Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Vehicles

Doctoral thesis (2018)

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer ... [more ▼]

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrating all these aspects is the development of advanced driver assistance systems to make electric vehicles more intelligent. Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software-enabled control functionality to automatically support the human driver. Driver assistance for electric vehicles will differ from the already used systems in fuel-powered cars such as electronic stability programs, adaptive cruise control etc. in a way that they must support energy efficiency while the system itself must also have a low power consumption. In this work, an eco-driving functionality as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car, navigation information as well as advanced information about the environment coming from sensors and network connections, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize energy consumption. From an algorithmic point of view, a stochastic model predictive control approach will be applied and adapted to the special constraints of the problem. Finally, the solution will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT Automation & Robotics Research Group (SnT ARG). [less ▲]

Risk-averse Stochastic Nonlinear Model Predictive Control for Real-time Safety-critical Systems

in The 20th World Congress of the International Federation of Automatic Control, IFAC 2017 World Congress, Toulouse, France, 9-14 July 2017 (2017, July 11)

Stochastic nonlinear model predictive control has been developed to systematically find an optimal decision with the aim of performance improvement in dynamical systems that involve uncertainties. However ... [more ▼]

Stochastic nonlinear model predictive control has been developed to systematically find an optimal decision with the aim of performance improvement in dynamical systems that involve uncertainties. However, most of the current methods are risk-neutral for safety-critical systems and depend on computationally expensive algorithms. This paper investigates on the risk-averse optimal stochastic nonlinear control subject to real-time safety-critical systems. In order to achieve a computationally tractable design and integrate knowledge about the uncertainties, bounded trajectories generated to quantify the uncertainties. The proposed controller considers these scenarios in a risk-sensitive manner. A certainty equivalent nonlinear model predictive control based on minimum principle is reformulated to optimise nominal cost and expected value of future recourse actions. The capability of proposed method in terms of states regulations, constraints fulfilment, and real-time implementation is demonstrated for a semi-autonomous ecological advanced driver assistance system specified for battery electric vehicles. This system plans for a safe and energy-efficient cruising velocity profile autonomously. [less ▲]