An official website of the United States government
Abstract To extend the operation window of batteries, active cell balancing has been studied in the literature. However, such an advancement presents significant computational challenges on real-time optimal control, especially when the number of cells in a battery increases. This article investigates the use of reinforcement learning (RL) and model predictive control (MPC) to effectively balance battery cells while at the same time keeping the computational load at a minimum. Specifically, event-triggered MPC is introduced as a way to reduce real-time computation. Different from the existing literature where rule-based or threshold-based event-trigger policies are used to determine the event instances, deep RL is explored to learn and optimize the event-trigger policy. Simulation results demonstrate that the proposed framework can keep the cell state-of-charge variation under 1% while using less than 1% computational resources compared to conventional MPC.
more »
« less
This content will become publicly available on September 1, 2026
Comparison of Linear MPC and Explicit MPC for Battery Cell Balancing Control
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
- Award ID(s):
- 2237317
- PAR ID:
- 10652031
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Algorithms
- Volume:
- 18
- Issue:
- 9
- ISSN:
- 1999-4893
- Page Range / eLocation ID:
- 548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
null (Ed.)In modern high-performance aircraft, the Fuel Thermal Management System (FTMS) plays a critical role in the overall thermal energy management of the aircraft. Actuator and state constraints in the FTMS limit the thermal endurance and capabilities of the aircraft. Thus, an effective control strategy must plan and execute optimized transient fuel mass and temperature trajectories subject to these constraints over the entire course of operation. For the control of linear systems, hierarchical Model Predictive Control (MPC) has shown to be an effective approach to coordinating both short- and long-term system operation with reduced computational complexity. However, for controlling nonlinear systems, common approaches to system linearization may no longer be effective due to the long prediction horizons of upper-level controllers. This paper explores the limitations of using linear models for hierarchical MPC of the nonlinear FTMS found in aircraft. Numerical simulation results show that linearized models work well for lower-level controllers with short prediction horizons but lead to significant reductions in aircraft thermal endurance when used for upper-level controllers with long prediction horizons. Therefore, a mixed-linearity hierarchical MPC formulation is presented with a nonlinear upper-level controller and a linear lower-level controller to achieve both high performance and high computational efficiency.more » « less
-
Malkin, T. (Ed.)Existing approaches to secure multiparty computation (MPC) require all participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities, resulting in computations spanning several hours or days. Such scenarios call for a dynamic participation model for MPC where participants have the flexibility to go offline as needed and (re)join when they have available computational resources. Such a model would also democratize access to privacy-preserving computation by facilitating an “MPC-as-a-service” paradigm—the deployment of MPC in volunteer-operated networks (such as blockchains, where dynamism is inherent) that perform computation on behalf of clients. In this work, we initiate the study of fluid MPC, where parties can dynamically join and leave the computation. The minimum commitment required from each participant is referred to as fluidity, measured in the number of rounds of communication that it must stay online. Our contributions are threefold: We provide a formal treatment of fluid MPC, exploring various possible modeling choices. We construct information-theoretic fluid MPC protocols in the honest-majority setting. Our protocols achieve maximal fluidity, meaning that a party can exit the computation after receiving and sending messages in one round. We implement our protocol and test it in multiple network settings.more » « less
-
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
