More Like this

1. 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. 

   more » « less
2. Battery Internal Short Detection Methodology Using Cell Swelling Measurements

   https://doi.org/10.23919/ACC45564.2020.9147956  

   Cai, Ting; Pannala, Sravan; Stefanopoulou, Anna G.; Siegel, Jason B. (July 2020, 2020 American Control Conference (ACC))

   Li-ion battery internal short circuits are a major safety issue for electric vehicles, and can lead to serious consequences such as battery thermal runaway. An internal short can be caused by mechanical abuse, high temperature, overcharging, and lithium plating. The low impedance or hard internal short circuit is the most dangerous kind. The high internal current flow can lead to battery temperature increase, thermal runaway, and even explosion in a few seconds. Algorithms that can quickly detect such serious events with a high confidence level and which are robust to sensor noise are needed to ensure passenger safety. False positives are also undesirable as many thermal runaway mitigation techniques, such as activating pyrotechnic safety switches, would disable the vehicle. Conventional methods of battery internal short detection, including voltage and surface temperature based algorithms, work well for a single cell. However, these methods are difficult to apply in large scale battery packs with many parallel cells. In this study, we propose a new internal short detection method by using cell swelling information during the early stages of a battery heating caused by an internal short circuit. By measuring cell expansion force, higher confidence level detection can be achieved for an internal short circuit in an electric vehicle scale battery pack. 

   more » « less
3. Estimation of Parameter Probability Distributions for Lithium-Ion Battery String Models Using Bayesian Methods

   https://doi.org/10.1115/DSCC2020-3218  

   Couto, Luis D.; Zhang, Dong; Aitio, Antti; Moura, Scott; Howey, David (October 2020, ASME 2020 Dynamic Systems and Control Conference)

   Abstract This paper addresses the parameter estimation problem for lithium-ion battery pack models comprising cells in series. This valuable information can be exploited in fault diagnostics to estimate the number of cells that are exhibiting abnormal behaviour, e.g. large resistances or small capacities. In particular, we use a Bayesian approach to estimate the parameters of a two-cell arrangement modelled using equivalent circuits. Although our modeling framework has been extensively reported in the literature, its structural identifiability properties have not been reported yet to the best of the authors’ knowledge. Moreover, most contributions in the literature tackle the estimation problem through point-wise estimates assuming Gaussian noise using e.g. least-squares methods (maximum likelihood estimation) or Kalman filters (maximum a posteriori estimation). In contrast, we apply methods that are suitable for nonlinear and non-Gaussian estimation problems and estimate the full posterior probability distribution of the parameters. We study how the model structure, available measurements and prior knowledge of the model parameters impact the underlying posterior probability distribution that is recovered for the parameters. For two cells in series, a bimodal distribution is obtained whose modes are centered around the real values of the parameters for each cell. Therefore, bounds on the model parameters for a battery pack can be derived. 

   more » « less
4. Development of high-voltage and high-energy membrane-free nonaqueous lithium-based organic redox flow batteries

   https://doi.org/10.1038/s41467-023-40374-y  

   Gautam, Rajeev K.; Wang, Xiao; Lashgari, Amir; Sinha, Soumalya; McGrath, Jack; Siwakoti, Rabin; Jiang, Jianbing “Jimmy” (August 2023, Nature Communications)

   Abstract Lithium-based nonaqueous redox flow batteries (LRFBs) are alternative systems to conventional aqueous redox flow batteries because of their higher operating voltage and theoretical energy density. However, the use of ion-selective membranes limits the large-scale applicability of LRFBs. Here, we report high-voltage membrane-free LRFBs based on an all-organic biphasic system that uses Li metal anode and 2,4,6-tri-(1-cyclohexyloxy-4-imino-2,2,6,6-tetramethylpiperidine)-1,3,5-triazine (Tri-TEMPO), N-propyl phenothiazine (C3-PTZ), and tris(dialkylamino)cyclopropenium (CP) cathodes. Under static conditions, the Li||Tri-TEMPO, Li||C3-PTZ, and Li||CP batteries with 0.5 M redox-active material deliver capacity retentions of 98%, 98%, and 92%, respectively, for 100 cycles over ~55 days at the current density of 1 mA/cm²and a temperature of 27 °C. Moreover, the Li||Tri-TEMPO (0.5 M) flow battery delivers an initial average cell discharge voltage of 3.45 V and an energy density of ~33 Wh/L. This flow battery also demonstrates 81% of capacity for 100 cycles over ~45 days with average Coulombic efficiency of 96% and energy efficiency of 82% at the current density of 1.5 mA/cm²and at a temperature of 27 °C. 

   more » « less
5. Machine learning enhanced control co-design optimization of an immersion cooled battery thermal management system

   https://doi.org/10.1063/5.0201438  

   Liu, Zheng; Kabirzadeh, Pouya; Wu, Hao; Fu, Wuchen; Qiu, Haoyun; Miljkovic, Nenad; Li, Yumeng; Wang, Pingfeng (July 2024, Journal of Applied Physics)

   The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption. 

   more » « less