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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles
In this paper we demonstrate a novel alternating direction method of
multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy
management problem considering both power split and engine on/off decisions.
The solution of a convex relaxation of the problem is used to initialize the
optimization, which is necessarily nonconvex, and whilst only local convergence
can be guaranteed, it is demonstrated that the algorithm will terminate with
the optimal power split for the given engine switching sequence. The algorithm
is compared in simulation against a charge-depleting/charge-sustaining (CDCS)
strategy and dynamic programming (DP) using real world driver behaviour data,
and it is demonstrated that the algorithm achieves 90\% of the fuel savings
obtained using DP with a 3000-fold reduction in computational time
The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle
The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%β9% being realizable
Time-optimal Control Strategies for Electric Race Cars with Different Transmission Technologies
This paper presents models and optimization methods to rapidly compute the
achievable lap time of a race car equipped with a battery electric powertrain.
Specifically, we first derive a quasi-convex model of the electric powertrain,
including the battery, the electric machine, and two transmission technologies:
a single-speed fixed gear and a continuously variable transmission (CVT).
Second, assuming an expert driver, we formulate the time-optimal control
problem for a given driving path and solve it using an iterative convex
optimization algorithm. Finally, we showcase our framework by comparing the
performance achievable with a single-speed transmission and a CVT on the Le
Mans track. Our results show that a CVT can balance its lower efficiency and
higher weight with a higher-efficiency and more aggressive motor operation, and
significantly outperform a fixed single-gear transmission.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and
Propulsion Conferenc
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