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1. Nonlinear Observer and Simplified Equivalent Circuit Model-based EKF-SOC Estimator of a Rechargeable LiFePo4 cell

   Park, Jinhyeong ; Kim, Gunwoo ; Na, Woonki ; Lim, Cheolwoo ; Kim, Jonghoon ( May 2019 , IEEE ICPE 2019 conference in Busan, Korea)

   The lithium iron phosphate (LFP) battery has more nonlinear characteristic than other battery type. For this reason, when we use electrical equivalent circuit model and the extended Kalman filter (EKF) for estimating the SOC, the estimation performance can be decreased in the nonlinear region. This paper proposes an advance estimation method of state of charge (SOC) for lithium iron phosphate (LFP) batteries. To improve the model accuracy, this paper utilizes the nonlinear observer for identifying the internal parameters of batteries. Furthermore, to reduce the nonlinear effect of the LFP batteries, this paper recast the Kalman process. Therefore, through the proposed method, the performance of SOC estimation can be more accurate and the computational burden is decreased when we apply the embedded system. 

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2. Fast and Simplified Algorithms for SoC and SoH Estimation of Vanadium Redox Flow Batteries

   Khaki, Bahman ; Das, Pritam ( November 2021 , IEEE Green Technologies Conference)

   Abstract— Typically, the electrochemical model and Equivalent Circuit Model-based (ECM) algorithms of Vanadium Redox Flow Batteries (VRFB) are complex and require high computation-time, thus not suitable to be used in the Battery Management Systems (BMS). Therefore, two simplified fast ECM-based estimation algorithms are proposed for the VRFB’s State of Charge (SoC) estimation. The methods are proposed based on two different parameter identification algorithms, namely discharge pulse response and the optimization-based parameter identification for the first-order ECM. The proposed approaches are further extended by an innovative, simplified mathematical model for the capacity fade of VRFBs based on the battery's electrochemical model. The simplified capacity loss model facilitates non-complex and fast estimation of VRFB’s State of Health (SoH), useful for modeling in the BMS. This has been led to a more accurate SoC estimation in the long-term use of the battery when the VRFB’s capacity fades due to electrolyte volume loss. Although the proposed joint estimation of VRFB’s SoC and SoH estimations are simpler to be modeled in the BMS, the proposed estimations are still accurate since the models consider enough electrochemical details of VRFBs. The accuracy, less complexity, reduced computation-time, and lower BMS memory storage highlight the proposed algorithms. Keywords—Battery Management System; Battery Parameter Estimation; Energy Storage Systems; Capacity Fade; State of Charge; State of Health; Vanadium Redox Flow Batteries. 

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3. 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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4. Sensorless parameter estimation of vanadium redox flow batteries in charging mode considering capacity fading

   Khaki, Bahman ; Das, Pritam ( November 2921 , Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   ABSTRACT State of Charge (SoC) and discharge capacity of the batteries are parameters that cannot be determined directly from the battery monitoring and control system and requires estimation. Current and voltage sensors have inherent error and delay leading to inaccurate measurements leading to inaccurate SoC and discharge capacity estimations. These sensors also have an additional cost to the battery system. This paper proposes a sensorless approach to estimate parameters of Vanadium Redox Flow Batteries (VRFBs) for both CC and CV charging methods by estimating battery current in CV mode and terminal voltage in CC mode. The results of estimations by the sensorless approach show a maximum relative error of 0.0035 in estimating terminal voltage in CC charging and a maximum relative error of 0.045 in estimating charging current in CV mode. Furthermore, long- term operation of vanadium redox flow batteries causes ion diffusions across the membrane and the depletion of active materials, which leads to capacity fading in VRFBs and inaccurate SoC estimation. To address the inaccuracy of SoC estimation in the long-term use of the battery, the capacity fading model is also considered for VRFBs in this paper. Experimental results show a 19% electrolyte volume change in the positive and negative tanks after 200 cycles of charge/discharge due to the bulk electrolyte transfer between the positive and negative sides of the battery system. This change of electrolyte volume results in 13.73% capacity fading after 200 cycles of charging/discharging. The SoC also changes by 7.1% after 200 cycles, due to the capacity and electrolyte volume loss, which shows the necessity of considering capacity fading in long-term use of the battery. 

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5. A Noise Suppression of LSTM algorithm combined with Kalman filter for Agriculture Automation

   https://doi.org/10.1145/3589883.3589918  

   Barry, Abdoulaye ; Kim, Junwhan ; Yu, Byunggu ; O'Hara, Sabine ( March 2023 , ACM)

   An immense volume of data is produced by sensor devices in the fields of aquaponics, hydroponics, and soil-based food production, where these devices track various environmental factors. Data stream mining is the method of retrieving data from fast-sampled data sources that are constantly streaming. The accuracy of data obtained through data stream mining is largely determined by the algorithm utilized to filter out noise. For threshold-based automation, an actuator can be activated when the value of sensor data is above a permissible threshold. Noise from sensors may activate the actuator. Several statistical and machine learning-based noise-suppression algorithms have been proposed in the literature. They have been evaluated based on the mean squared error metric (MSE). The Long Short-Term Memory – LSTM filter (MSE: 0.000999943) performs better noise suppression than other traditional filters – Kalman (MSE: 0.0015982). We propose a new noise suppression filter – LSTM combined with Kalman (LSTM-KF). In LSTM-KF, the Kalman filter acts as an encoder and the LSTM becomes the decoder, resulting in a significantly lower MSE – 0.000080789592. The LSTM-KF is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost. 

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