Neural Pontryagin Optimal Controller for Lossy Energy Storage with Nonlinear Efficiency
Year: 2025
Type: conference-paper
Abstract: Solving real-world lossy battery control problems are challenging tasks, as the complex charging dynamics involving nonlinear efficiency are usually unrevealed to the system operators. It is thus hard to find the optimal control actions numerically. To deal with such modeling and computation challenges, in this paper, we integrate Neural Networks with the Pontryagin's Maximum Principle (PMP), and propose a sample efficient framework Neural-PMP. The resulting controller can be implemented for systems with unknown and complex dynamics. By taking an iterative approach, the proposed framework not only utilizes the accurate surrogate models parameterized by neural networks, it also efficiently recovers the optimality conditions along with the optimal action sequences via PMP conditions. Numerical simulations on energy arbitrage of grid-connected lossy battery tasks demonstrate our proposed Neural-PMP is a general and versatile computation tool for finding optimal solutions. And compared with the widely applied model-free and model-based reinforcement learning (RL) algorithms, our Neural-PMP achieves higher sample-efficiency and performance in terms of control objectives. We also open-source our Neural-PMP implementations for achieving more competitive energy storage control.
Authors: Chengyang Gu, Hui Xiong, Yize Chen
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415399303