Estimasi State of Charge (SOC) Pada Baterai Lithium – Ion Menggunakan Feed-Forward Backpropagation Neural Network Dua Tingkat

Pada perkembangan teknologi zaman ini, baterai memainkan peran penting dalam memenuhi kebutuhan energi. Faktor yang mempengaruhi kinerja baterai adalah keadaan muatan dan energi yang disimpan baterai terbatas. Hal ini menyebabkan kerusakan baterai karena pengisian dan pengosongan baterai yang berulang kali dan dapat over-charge atau over-discharging. Oleh karena itu, dibutuhkan alat pengukuran kapasitas baterai untuk menjaga agar baterai tidak cepat rusak. State of charge baterai adalah status yang menunjukkan kapasitas baterai.  Pada penelitian ini, akan dilakukan estimasi state of charge baterai Lithium Ion 12 volt 4 ah menggunakan metode Feed-Forward Backpropagation karena metode FF-BPNN dapat menyesuaikan dengan karakteristik non-linear dari baterai Dalam metode ini, ada dua tingkat proses training data (dua neural) untuk mendapatkan estimasi nilai OCV dan SOC. Tingkat pertama dengan parameter input yaitu tegangan, arus, dan waktu charge atau discharge untuk estimasi OCV. OCV dari hasil tingkat pertama, digunakan sebagai input dari proses tingkat kedua untuk estimasi SOC. Hasil estimasi SOC yang didapat yaitu jumlah nilai hidden neuron 11 pada kondisi charging dan nilai hidden neuron 10 pada kondisi discharge, karena hal itu menunjukkan bahwa estimasi baterai lithium-ion SOC dengan pembacaan aktualnya menunjukkan error yang kecil

State Estimation in Lithium-ion Batteries Using Pulse Perturbation and Feedforward Neural Networks

Predicting battery stored charge, available capacity, and peak power quickly and accurately is important for understanding pack performance and stability. It is proposed that a feedforward neural network (FNN) can estimate this information using cell voltage response to an injected current. Voltage response varies with the internal chemistry, represented by charge, capacity, and impedance. These characteristics are quantified here using state of charge (SoC), state of energy (SoE), and state of power (SoP). Cell response data is collected for various states at constant temperature, resulting in 234 unique voltage responses for training and evaluating the FNN. Training is performed using 3 distinct variations on the data: (1) the full voltage response, (2) individual portions of the response, such as charging or relaxation periods, and (3) fractions of the charge and discharge periods ranging from one-half to a single open-circuit voltage measurement. Using the full response, the average mean absolute error (MAE) is 0.0057 for SoE estimation. The average MAE is below 0.0080 for SoC and SoP estimation. The results for pulse portions show that Charge-rest or Discharge-rest responses perform almost as well as the full pulse. This may inform future pulse design for further optimization. The results for pulse fractions show that error increases as the amount of input data decreases, which validates the hypothesis that pulse perturbation yields high performance in FNN. The technique can be expanded to other temperatures, with potential for estimation of other states, and even degradation mechanisms. Estimation requires 3 minutes of voltage and current data, with no charging history needed and low computational complexity. The proposed method is thus suitable for development of advanced battery management systems in electric vehicles

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation