An official website of the United States government
Large-scale battery energy storage systems (BESS) play a pivotal role in advancing sustainability through their widespread applications in electrified transportation, power grids, and renewable energy systems. However, achieving optimal power management for these systems poses significant computational challenges. To address this, we propose a scalable approach that partitions the cells of a large-scale BESS into clusters based on state-of-charge (SoC), temperature, and internal resistance. Each cluster is represented by a model that approximates its collective SoC and temperature dynamics and overall power losses during charging and discharging. Using these clusters, we formulate a receding-horizon optimal power control problem to minimize power losses while promoting SoC and temperature balancing. The optimization determines a power quota for each cluster, which is then distributed among its constituent cells. This clustering approach drastically reduces computational costs by working with a smaller number of clusters instead of individual cells, enabling scalability for large-scale BESS. Simulations show a computational overhead reduction of over 60% for small-scale and 98% for large-scale BESS compared to conventional cell-level optimization. Experimental validation using a 20-cell prototype further underscores the approach's effectiveness and practical utility.
more »
« less
Stochastic Drift Counteraction Optimal Control of a Fuel Cell-Powered Small Unmanned Aerial Vehicle
This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a 1.5 kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge (SOC) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial SOC value. If starting from a high SOC value, the battery energy is used till a turnpike value of the SOC is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to 2.7 h of flight time.
more »
« less
- Award ID(s):
- 1931738
- PAR ID:
- 10274635
- Date Published:
- Journal Name:
- Energies
- Volume:
- 14
- Issue:
- 5
- ISSN:
- 1996-1073
- Page Range / eLocation ID:
- 1304
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This paper presents a practical experiment for estimating the state-of-charge (SOC) of individual cells in a series-connected heterogeneous lithium-ion battery pack, where only the terminal voltage of the battery pack is measured. To deal with real-time computation constraints, the dense extended Kalman filter (DEKF) algorithm has been proposed in the literature, which has a significantly lower computational complexity compared to the regular extended Kalman filter for this specific estimation problem. This work supplements the existing work by conducting a real-world experiment to validate the performance of the DEKF. Specifically, experiments involving a battery pack of three cells connected in series were conducted, where the battery pack was discharged under a constant current load. A genetic algorithm was applied to identify missing model parameters, as well as tuning the DEKF for optimal convergence and accurate SOC estimation. Our experimental results confirm that the proposed DEKF accurately estimates the SOC of each cell regardless of the hardware limitations and uncertainty, making it suitable for low-cost, real-time battery management systems. In particular, the SOC estimation error can be kept well under 1% even if the initial estimate is far from the true SOC.more » « less
-
Optimal power management of battery energy storage systems (BESS) is crucial for their safe and efficient operation. Numerical optimization techniques are frequently utilized to solve the optimal power management problems. However, these techniques often fall short of delivering real-time solutions for large-scale BESS due to their computational complexity. To address this issue, this paper proposes a computationally efficient approach. We introduce a new set of decision variables called power-sharing ratios corresponding to each cell, indicating their allocated power share from the output power demand. We then formulate an optimal power management problem to minimize the system-wide power losses while ensuring compliance with safety, balancing, and power supply-demand match constraints. To efficiently solve this problem, a parameterized control policy is designed and leveraged to transform the optimal power management problem into a parameter estimation problem. We then implement the ensemble Kalman inversion to estimate the optimal parameter set. The proposed approach significantly reduces computational requirements due to 1) the much lower dimensionality of the decision parameters and 2) the estimation treatment of the optimal power management problem. Finally, we conduct extensive simulations to validate the effectiveness of the proposed approach. The results show promise in accuracy and computation time compared with explored numerical optimization techniques.more » « less
-
Distributed hybrid and electric propulsion systems are one of the most promising technologies to reduce aircraft emissions, resulting in research efforts to investigate new architectures and the design of optimal energy management strategies. This work defines the optimal requirements in terms of battery pack sizing and cell technology for a hybrid-electric regional wing-mounted distributed propulsion aircraft through the application of a design space exploration method. The propulsion system considered in this study is a series-parallel hybrid turboelectric power train with distributed electric fans. A set of six lithium-ion battery cell technologies was identified and experimentally characterized, including both commercially available and prototype cells at different combinations of specific energy and power. A model of the aircraft was developed and used to define the optimal energy management strategy for the hybrid turboelectric propulsion system, which was solved using dynamic programming. The design space exploration was conducted by varying the cell technology and battery storage system size; and the effects on fuel consumption, energy management strategy, and thermal management were compared.more » « less
-
This paper presents and compares two model predictive control (MPC) approaches for battery cell state-of-charge (SOC) balancing. In both approaches, a linearized discrete-time model that takes into account individual cell capacities is used. The first approach is a linear MPC controller that effectively regulates multiple cells to track a target SOC level while satisfying physical constraints. The second approach is based on explicit MPC implementation to reduce online computation while achieving a comparable performance. The simulation results suggest that explicit MPC can deliver the same balancing performance as linear MPC, while achieving faster online execution. Specifically, explicit MPC reduces the computation time by 37.3% in a five-cell battery example, with the cost of higher offline computation. However, simulation results also reveal a significant limitation for explicit MPC for battery systems with a larger number of cells. As the number of cells increases and/or the prediction horizon increases, the computational requirements grow exponentially, making its application to SOC balancing for large battery systems impractical. To the best of the authors’ knowledge, this is the first study that compares MPC and explicit MPC algorithms in the context of battery cell balancing.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/en14051304
