scholarly journals State of Charge Estimation Method for Lithium-Ion Batteries in All-Electric Ships Based on LSTM Neural Network

2020 ◽  
Vol 27 (3) ◽  
pp. 100-108
Author(s):  
Pan Geng ◽  
Xiaoyan Xu ◽  
Tomasz Tarasiuk
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Error ◽  
Lithium Ion ◽  
Clean Energy ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
The Neural Network ◽  
Hidden Layer

AbstractAll-electric ships (AES) are considered an effective solution for reducing greenhouse gas emissions as they are a platform to use clean energy sources such as lithium-ion batteries, fuel cells and solar cells instead of fossil fuel. Even though these batteries are a promising alternative, the accuracy of the battery state of charge (SOC) estimation is a critical factor for their safe and reliable operation. The SOC is a key indicator of battery residual capacity. Its estimation can effectively prevent battery over-discharge and over-charge. Next, this enables reliable estimation of the operation time of fully electric ferries, where little time is spent at the harbour, with limited time available for charging. Thus, battery management systems are essential. This paper presents a neural network model of battery SOC estimation, using a long short-term memory (LSTM) recurrent neural network (RNN) as a method for accurate estimation of the SOC in lithium-ion batteries. The current, voltage and surface temperature of the batteries are used as the inputs of the neural network. The influence of different numbers of neurons in the neural network’s hidden layer on the estimation error is analysed, and the estimation error of the neural network under different training times is compared. In addition, the hidden layer is varied from 1 to 3 layers of the LSTM nucleus and the SOC estimation error is analysed. The results show that the maximum absolute SOC estimation error of the LSTM RNN is 1.96% and the root mean square error is 0.986%, which validates the feasibility of the method.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

scholarly journals State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1546
Author(s):  
M. S. Hossain Lipu ◽  
M. A. Hannan ◽  
Aini Hussain ◽  
Afida Ayob ◽  
Mohamad H. M. Saad ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Optimal Number ◽  
State Of Charge ◽  
Optimization Approach ◽  
Safe Driving ◽  
Soc Estimation ◽  
Improved Firefly Algorithm

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

scholarly journals State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1012 ◽  
Author(s):  
Yidan Xu ◽  
Minghui Hu ◽  
Chunyun Fu ◽  
Kaibin Cao ◽  
Zhong Su ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Second Order ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Temperature Dependent ◽  
Soc Estimation

Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 306
Author(s):  
Shuqing Li ◽  
Chuankun Ju ◽  
Jianliang Li ◽  
Ri Fang ◽  
Zhifei Tao ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
High Efficiency ◽  
Lithium Ion ◽  
Clean Energy ◽  
Degradation Process ◽  
State Of Charge ◽  
Average Error ◽  
Estimation Model ◽  
Battery Degradation

Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input feature but also takes into account the influence of battery degradation process, including charging and discharging times, as well as the last discharge charge. The SOC of the battery is estimated by the network under three different working conditions, and the results show that the average error of the proposed neural network is less than 3%. Compared with other neural network structures, the proposed network estimation results are more stable and accurate.

scholarly journals Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy

2021 ◽  
Vol 9 (11) ◽  
pp. 1228
Author(s):  
Seongwan Kim ◽  
Jongsu Kim
Keyword(s):  
Neural Network ◽  
Hybrid System ◽  
Control Method ◽  
The State ◽  
State Of Charge ◽  
Energy Control ◽  
Optimal Energy ◽  
The Neural Network ◽  
Equivalent Consumption Minimization Strategy ◽  
Hidden Layer

This paper introduces an optimal energy control method whose rule-based control employs the equivalent consumption minimization strategy as the design standard to support a neural network technique. Using the proposed control method, the output command values for each power source based on the load of the ship and the state of charge of the battery satisfy the target of energy optimization. Based on the rules, the load of the ship and the state of charge of the battery were the input in the neural network, and the outputs of two generators were recorded as the output values of the neural network. To optimize the weights of the neural network and reduce the error between the predicted values and results, the Bayesian regularization method was employed, and a single hidden layer with 20 nodes, 2 input layers, and 2 output layers were considered. For the hidden layer, the tansigmoid function was applied, and for the activation functions of the output layers, linear functions were adopted considering the correlation between the input and output data used for training the neural network. The propulsion motor was fitted with a speed controller to ensure a stable speed, and a torque load was applied on the propulsion motor. To verify the accuracy of the neural network learning, a generator–battery hybrid system simulation was conducted using MATLAB Simulink, and the neural network learned values were compared with the generator output command values obtained based on the load of the ship and the battery state of charge. Additionally, it was confirmed that the generator command values were consistent with the neural network learned values, and the stability of the system was maintained by controlling the speed, voltage, and current control of the propulsion motor under various loads of the ship and different battery charge statuses.

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