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Predictive Energy Management for Hybrid Powertrains

Satish Vedula, Olugbenga Anubi

TL;DR

The paper tackles battery degradation in hybrid powertrains by formulating a health-aware, distributed model predictive energy management (MPEM) framework. It integrates a degradation heuristic based on battery power usage into a two-node (engine and battery) ADMM-based optimization, ensuring real-time feasibility through closed-form subproblem solutions and an aggregator mechanism. The approach is validated via realistic simulations and real-time hardware-in-the-loop experiments across three platforms: HEV, dynamically positioned ships, and hybrid electric aircraft, showing that higher emphasis on battery health (via the weight $oldsymbol{ ext{gamma}}$) reduces degradation at the expense of some power-tracking performance. This method offers a scalable, plug-and-play solution for multi-energy hybrid systems, with demonstrated potential to extend battery life without sacrificing overall engine efficiency in practice.

Abstract

Hybrid power trains (HPT) run on multiple energy sources, often involving energy storage systems/batteries (ESS). As a result, the risk of battery degradation and the reliability of energy storage elements pose a major challenge in designing an energy-efficient hybrid power train. This paper presents an energy management strategy that adaptively splits power demand between the engine and the battery pack in a hybrid power train taking into account the battery degradation. Incorporating the battery degradation model directly into the underlying optimization problem is challenging on multiple fronts: 1) Any reasonable degradation model will, due to its complexity, result in a complicated optimization problem that is impractical for real-time implementation 2) the models contain a lot of time-varying parameters that can only be determined through destructive experimental procedures. As a result, it is essential to devise heuristics that reasonably capture the degradation per usage of the batteries. One such heuristic considered in this paper is the absolute power extracted from the battery. A distributed model predictive strategy is then developed to coordinate the power split to maximize efficiency while mitigating the failure risk due to battery degradation. The designed EM strategy is demonstrated through a realistic simulation of three different hybrid power trains: hybrid road vehicles (for example: a hybrid electric vehicle (HEV)), hybrid surface vehicles (for example: dynamically positioned hybrid ships (DPS)), and hybrid aerial vehicles (for example: hybrid electric aircraft (HEA)). The results show the effectiveness of the energy management strategy in managing battery degradation.

Predictive Energy Management for Hybrid Powertrains

TL;DR

The paper tackles battery degradation in hybrid powertrains by formulating a health-aware, distributed model predictive energy management (MPEM) framework. It integrates a degradation heuristic based on battery power usage into a two-node (engine and battery) ADMM-based optimization, ensuring real-time feasibility through closed-form subproblem solutions and an aggregator mechanism. The approach is validated via realistic simulations and real-time hardware-in-the-loop experiments across three platforms: HEV, dynamically positioned ships, and hybrid electric aircraft, showing that higher emphasis on battery health (via the weight ) reduces degradation at the expense of some power-tracking performance. This method offers a scalable, plug-and-play solution for multi-energy hybrid systems, with demonstrated potential to extend battery life without sacrificing overall engine efficiency in practice.

Abstract

Hybrid power trains (HPT) run on multiple energy sources, often involving energy storage systems/batteries (ESS). As a result, the risk of battery degradation and the reliability of energy storage elements pose a major challenge in designing an energy-efficient hybrid power train. This paper presents an energy management strategy that adaptively splits power demand between the engine and the battery pack in a hybrid power train taking into account the battery degradation. Incorporating the battery degradation model directly into the underlying optimization problem is challenging on multiple fronts: 1) Any reasonable degradation model will, due to its complexity, result in a complicated optimization problem that is impractical for real-time implementation 2) the models contain a lot of time-varying parameters that can only be determined through destructive experimental procedures. As a result, it is essential to devise heuristics that reasonably capture the degradation per usage of the batteries. One such heuristic considered in this paper is the absolute power extracted from the battery. A distributed model predictive strategy is then developed to coordinate the power split to maximize efficiency while mitigating the failure risk due to battery degradation. The designed EM strategy is demonstrated through a realistic simulation of three different hybrid power trains: hybrid road vehicles (for example: a hybrid electric vehicle (HEV)), hybrid surface vehicles (for example: dynamically positioned hybrid ships (DPS)), and hybrid aerial vehicles (for example: hybrid electric aircraft (HEA)). The results show the effectiveness of the energy management strategy in managing battery degradation.
Paper Structure (13 sections, 3 theorems, 40 equations, 12 figures, 2 tables)

This paper contains 13 sections, 3 theorems, 40 equations, 12 figures, 2 tables.

Table of Contents

  1. Abstract
  2. Introduction
  3. Notations
  4. Model Development
  5. Energy Storage System Modeling
  6. Proposed Energy Management Method
  7. Optimization Problem at Engine Node
  8. Optimization problem at Battery Node
  9. Real-Time Numerical Simulation - Hybrid Electric Vehicle, dynamically positioned hybrid ship and hybrid electric aircraft
  10. Hybrid Electric Vehicle
  11. Dynamically Positioned Hybrid Ships
  12. Hybrid Electric Aircraft
  13. Conclusion

Key Result

Theorem 1

Consider the generalized vehicle dynamics in genaralized_dynamics, together with a differentiable reference trajectory generated by the stable dynamics $\dot{\mathbf{x}}_{d} = f(\mathbf{x}_d)$. Let be the associated speed tracking error. Given positive real numbers $k_1, \gamma_1$, the closed loop speed tracking error dynamics is globally asymptotically stable (GAS) under the control law where $

Figures (12)

  • Figure 1: Hybrid power-train schematic with different applications to road vehicles (hybrid electric cars), surface vehicles (dynamically positioned ships), and aerial vehicles (hybrid electric aircraft).
  • Figure 2: Distributed optimization implementation scheme
  • Figure 3: (a) Velocity and power required to track the velocity performance for the hybrid road vehicles, (b) Velocity and power required to track the velocity performance for the hybrid surface vehicles, (c) Velocity and power required to track the velocity performance for the hybrid aerial vehicles.
  • Figure 4: Distributed optimization implementation scheme
  • Figure 5: State of charge, engine optimal power, battery optimal power, and the power tracking for one simulation run for US06 drive-cycle and NYCC drive-cycle swapping the driving profiles.
  • ...and 7 more figures

Theorems & Definitions (11)

  • Theorem 1
  • proof
  • Remark 1
  • Remark 2: Hybrid road vehicles
  • Remark 3: Hybrid surface vehicles
  • Remark 4: Hybrid aerial Vehicles
  • Theorem 2
  • proof
  • Proposition 1
  • Remark 5
  • ...and 1 more