Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Stage of Charge Estimation of Lithium-ion Battery Packs Based on Improved Cubature Kalman Filter with Long Short-Term Memory Model

Accurate estimation of state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, firstly, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate battery cell’s SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation value from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited within 2% after convergence

Battery State-of-Charge Estimation Using Neural Networks

This thesis proposes a way to augment the existing machine learning algorithm applied to state-of-charge estimation by introducing a form of pulse injection to the running battery cells. It is believed that the information contained in the pulse responses can be interpreted by a machine learning algorithm whereas other techniques are difficult to decode due to the nonlinearity. OCV Mapping is also applied in order to evaluate and compare the performances with feedforward neural network (FNN) -based approach. Coulomb-counting is selected as the basis of making comparison as it is capable of obtaining the SoC with an error less than 0.1%. The detailed system layout is given to perform the augmented SoC estimation integrated in a real-world testbench. Testing procedures specifically designed for both OCV Mapping and FNN-based approach are also explained and provided. A 2-hidden layer FNN is trained to acquire the nonlinear relationship between the training pulse and the ground-truth SoC. The experimental data is trained and the results are shown within 6-8mins computation time and an error boundary of 1.13% for charge and 0.80% for discharge, whereas OCV Mapping has approximately 3.35% SoC estimation error for charge and 1.86% for discharge even after 90 minutes relaxation

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network

In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of theSoC

Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries.

To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of 0.17% and a root mean square error (RMSE) of 0.19% for SOC estimation, with a convergence time of 4 s. Under the DST condition, the corresponding values are 0.05% for MAE, 0.07% for RMSE, and 5 s for convergence time. Moreover, in this research, the SOH is described as having internal resistance. Under the Beijing bus DST condition, the MAE and the RMSE of the estimated internal resistance of the proposed approach are 0.018% and 0.075%, with the corresponding values of 0.014% and 0.043% under the DST condition. The results of the experiments provide empirical evidence for the challenges associated with the efficacious estimation of SOC and SOH