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1. An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging

   Peyman Mohtat, Sravan Pannala (October 2021, 2021 Modeling, Estimation and Control Conference)

   null (Ed.)

   Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge times is the accelerated aging of the battery at higher charging rates, which can be driven by lithium plating, increased solid electrolyte interphase growth due to elevated temperatures, and particle cracking due to mechanical stress. Lithium plating depends on the overpotential of the negative electrode, and mechanical stress depends on the concentration gradient, both of which cannot be measured directly. Techniques based on physics-based models of the battery and optimal control algorithms have been developed to this end. While these methods show promise in reducing degradation, their optimization algorithms' complexity can limit their implementation. In this paper, we present a method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CVησT (VEST). The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. We demonstrate the new CC-CVησT charging using an electrochemical model with mechanical and thermal effects included. Furthermore, we discuss how uncertainties can be accounted for by considering safety margins for the plating and stress constraints. 

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2. Reinforcement Learning Based Fast Charging of Electric Vehicle Battery Packs

   https://doi.org/10.4271/2023-01-1681  

   Abbasi, Mohammad Hossein; Arjmandzadeh_PhD, Ziba; Zhang, Jiangfeng; Xu, Bin; Krovi, Venkat N (October 2023, Society of Automotive Engineers International)

   Range anxiety and lack of adequate access to fast charging are proving to be important impediments to electric vehicle (EV) adoption. While many techniques to fast charging EV batteries (model-based & model-free) have been developed, they have focused on a single Lithium-ion cell. Extensions to battery packs are scarce, often considering simplified architectures (e.g., series-connected) for ease of modeling. Computational considerations have also restricted fast-charging simulations to small battery packs, e.g., four cells (for both series and parallel connected cells). Hence, in this paper, we pursue a model-free approach based on reinforcement learning (RL) to fast charge a large battery pack (comprising 444 cells). Each cell is characterized by an equivalent circuit model coupled with a second-order lumped thermal model to simulate the battery behavior. After training the underlying RL, the developed model will be straightforward to implement with low computational complexity. In detail, we utilize a Proximal Policy Optimization (PPO) deep RL as the training algorithm. The RL is trained in such a way that the capacity loss due to fast charging is minimized. The pack’s highest cell surface temperature is considered an RL state, along with the pack’s state of charge. Finally, in a detailed case study, the results are compared with the constant current-constant voltage (CC-CV) approach, and the outperformance of the RL-based approach is demonstrated. Our proposed PPO model charges the battery as fast as a CC-CV with a 5C constant stage while maintaining the temperature as low as a CC-CV with a 4C constant stage. 

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3. Safe and Efficient Reinforcement Learning using Disturbance-Observer-Based Control Barrier Functions

   Cheng, Yikun; Zhao, Pan; Hovakimyan, Naira (June 2023, Proceedings of The 5th Annual Learning for Dynamics and Control Conference)

   Matni, Nikolai; Morari, Manfred; Pappas, George J. (Ed.)

   Safe reinforcement learning (RL) with assured satisfaction of hard state constraints during training has recently received a lot of attention. Safety filters, e.g., based on control barrier functions (CBFs), provide a promising way for safe RL via modifying the unsafe actions of an RL agent on the fly. Existing safety filter-based approaches typically involve learning of uncertain dynamics and quantifying the learned model error, which leads to conservative filters before a large amount of data is collected to learn a good model, thereby preventing efficient exploration. This paper presents a method for safe and efficient RL using disturbance observers (DOBs) and control barrier functions (CBFs). Unlike most existing safe RL methods that deal with hard state constraints, our method does not involve model learning, and leverages DOBs to accurately estimate the pointwise value of the uncertainty, which is then incorporated into a robust CBF condition to generate safe actions. The DOB-based CBF can be used as a safety filter with model-free RL algorithms by minimally modifying the actions of an RL agent whenever necessary to ensure safety throughout the learning process. Simulation results on a unicycle and a 2D quadrotor demonstrate that the proposed method outperforms a state-of-the-art safe RL algorithm using CBFs and Gaussian processes-based model learning, in terms of safety violation rate, and sample and computational efficiency. 

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4. Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions

   Cheng, Yikun; Zhao, Pan; Hovakimyan, Naira (June 2023, Proceedings of Machine Learning Research)

   Safe reinforcement learning (RL) with assured satisfaction of hard state constraints during training has recently received a lot of attention. Safety filters, e.g., based on control barrier functions (CBFs), provide a promising way for safe RL via modifying the unsafe actions of an RL agent on the fly. Existing safety filter-based approaches typically involve learning of uncertain dynamics and quantifying the learned model error, which leads to conservative filters before a large amount of data is collected to learn a good model, thereby preventing efficient exploration. This paper presents a method for safe and efficient RL using disturbance observers (DOBs) and control barrier functions (CBFs). Unlike most existing safe RL methods that deal with hard state constraints, our method does not involve model learning, and leverages DOBs to accurately estimate the pointwise value of the uncertainty, which is then incorporated into a robust CBF condition to generate safe actions. The DOB-based CBF can be used as a safety filter with model-free RL algorithms by minimally modifying the actions of an RL agent whenever necessary to ensure safety throughout the learning process. Simulation results on a unicycle and a 2D quadrotor demonstrate that the proposed method outperforms a state-of-the-art safe RL algorithm using CBFs and Gaussian processes-based model learning, in terms of safety violation rate, and sample and computational efficiency. 

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5. Extending Battery Life via Load SExtending Battery Life via Load Sharing in Electric Aircraft

   https://doi.org/10.2514/6.2024-2154  

   Anthony, George; Peskar, Jarrett; Downey, Austin R.J.; Booth, Kristen (January 2024, AIAA SCITECH 2024 Forum)

   Electric Aircraft have the potential to revolutionize short-distance air travel with lower operating costs and simplified maintenance. However, due to the long lead-time associated with procuring batteries and the maintenance challenges of replacing and repairing batteries in electric aircraft, there are still unanswered questions related to the true long-term operating costs of electric aircraft. This research examines using a load-sharing system in electric aircraft to optimally tune battery degradation in a multi-battery system such that the battery life of a single battery is extended. The active optimization of energy drawn from multiple battery packs means that each battery pack reaches its optimal replacement point at the same time; thereby simplifying the maintenance procedure and reducing cost. This work uses lithium iron phosphate batteries experimentally characterized and simulated in OpenModelica for a flight load profile. Adaptive agents control the load on the battery according to factors such as state of charge, and state of health, to respond to potential faults. The findings in this work show the potential for adaptive agents to selectively draw more power from a healthy battery to extend the lifespan of a degraded battery such that the remaining useful life of both batteries reaches zero at the same time. Simulations show that dual battery replacement can be facilitated using the proposed method when the in-service battery has a remaining useful life of greater than 0.5; assuming that the replacement battery it is paired with has a remaining useful life of 1.0. Limitations of the proposed method are discussed within this work. 

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