scholarly journals Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programming

2021 ◽  
Author(s):  
Shreshta Rajakumar Deshpande ◽  
Shobhit Gupta ◽  
Abhishek Gupta ◽  
Marcello Canova
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Real World ◽  
Autonomous Vehicles ◽  
Energy Savings ◽  
Approximate Dynamic Programming ◽  
Connected Vehicle ◽  
Hybrid Powertrain ◽  
Embedded Controller ◽  
Hybrid Electric Powertrain

Abstract Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment. Approximate Dynamic Programming (ADP) is used to solve the Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for the RHOCP is approximated as the base-policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Online Powermanagement With Embedded Optimization for a Multi-Source Hybrid With Dynamic Power Sharing Between Components

2015 ◽  
Author(s):  
Bedatri Moulik ◽  
Dirk Söffker
Keyword(s):  
Real Time ◽  
Power Distribution ◽  
Primary Source ◽  
Multiple Objectives ◽  
Online Optimization ◽  
Power Sharing ◽  
Hybrid Powertrain ◽  
Optimal Power ◽  
Transportation Sector ◽  
Hybrid Electric Powertrain

The need for the development of online powermanagement strategies applicable to real-time hybrid powertrain systems is an important issue in the transportation sector. A powermanagement strategy alone does not necessarily ensure optimal power distribution amongst the drive train components. Thus optimization of powemanagement is another task which needs to be considered in terms of multiple objectives. Apart from online applicability of powermanagement optimization, a consideration of useful combinations of drivetrain components such as two storage elements together with a primary source may also be useful. In this contribution, an online powermanagement strategy is applied to a three-source hybrid electric powertrain. An optimization of controller parameters with embedded-online optimization is proposed.

scholarly journals Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

2020 ◽  
Vol 34 (01) ◽  
pp. 507-515
Author(s):  
Sanket Shah ◽  
Meghna Lowalekar ◽  
Pradeep Varakantham
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Value Function ◽  
Approximate Dynamic Programming ◽  
Linear Program ◽  
Combinatorial Complexity ◽  
Pooling Problem ◽  
Time Step ◽  
Current Time ◽  
On Demand

On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assigned one passenger at a time – in on-demand ride-pooling, each vehicle must simultaneously serve multiple passengers with heterogeneous origin and destination pairs without violating any quality constraints. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering the effect such an assignment could have on assignments in future time steps. However, considering the future effects of an assignment that also has to consider what combinations of passenger requests can be assigned to vehicles adds a layer of combinatorial complexity to the already challenging problem of considering future effects in the ToD case.A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, as the value update relies on dual values from the LP. The assignment problem in ride pooling requires an Integer Linear Program (ILP) that has bad LP relaxations. Therefore, our key technical contribution is in providing a general ADP method that can learn from the ILP based assignment found in ride-pooling. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach easily outperforms leading approaches for on-demand ride-pooling on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.

scholarly journals Control and Powertrain Management for Multi-Autonomous Hybrid Vehicles

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Masood Ghasemi ◽  
Xingyong Song
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Algebraic Equations ◽  
Computationally Efficient ◽  
Hybrid Powertrain ◽  
Fuel Consumption Rate ◽  
Optimal Approach

The need for less fuel consumption and the trend of higher level of autonomy together urge the power optimization in multihybrid autonomous vehicles. Both the multivehicle coordination control and the hybrid powertrain energy management should be optimized to maximize fuel savings. In this paper, we intend to have a computationally efficient framework to optimize them individually and then evaluate the overall control performance. The optimization is conducted in series. First is at the multivehicle system's level where the distributed locally optimal solution is given for vehicles with nonlinear dynamics. Second, the powertrain management optimization is conducted at the hybrid powertrain level. We provide an analytical formulation of the powertrain optimization for each hybrid vehicle by using Pontryagin's minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations, which enables the computationally efficient real-time implementation. To justify the applicability of the methodology in real-time, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach. An off-road example is given in the simulation, although the framework developed can be applied to on-road scenario as well.

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