SOC estimation of lithium-ion batteries for electric vehicles based on multimode ensemble SVR

Tian, Huixin; Li, Ang; Li, Xiaoyü

doi:10.1007/s43236-021-00279-9

Cited by 18 publications

(11 citation statements)

References 20 publications

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“…where 𝐻(𝑝) is the ensemble SOC prediction, 𝑤 𝑛 is the weight applied to base model 𝑛 , ℎ 𝑛 (𝑝) is the SOC prediction for base model 𝑛 , and ⍺ is a weight parameter that is set to 12 based on the optimization process in [5].…”

Section: Integration Of Ensemblementioning

confidence: 99%

“…However, these methods do not account for variations in driving conditions in real-time applications. [5] proposes an ensemble model that first clusters the training data, where a support vector regression (SVR) base model is built on each data subset. The prediction of each base model for a given time step is then weighted dynamically by the distance of the test data to the center of its training data subset.…”

Section: Introductionmentioning

confidence: 99%

“…This paper follows the method in [5] by proposing a novel Li-ion battery SOC estimation method through a Deep Feedforward Neural Network Multimode Ensemble (DNN-ME), where deep feedforward neural networks (DNN) are used in place of SVRs in the ensemble to increase prediction accuracy. The proposed ensemble model is expected to outperform a single DNN model and achieve good SOC estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Ensemble Method for Li-ion Battery State-of-Charge Estimation

Shenrun¹,

Zhang²

2022

Preprint

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Lithium-ion batteries (LIB) are vital components of modern electric vehicles and load management in smart grids due to their relatively high energy density, power density, and efficiency. It is important that onboard battery management systems in battery electric vehicles have accurate battery state of charge (SOC) information to gauge the remaining vehicle range and minimize battery degradation through power management. However, owing to highly dynamic vehicle driving habits and the nonlinear nature of SOC relative to other battery parameters such as current, voltage, and temperature, SOC is unable to be measured directly and is difficult to be accurately estimated in real-time. This article proposes a novel Li-ion battery SOC estimation method through a Deep Feedforward Neural Network Multimode Ensemble (DNN-ME). K-means clustering is used to separate the training data into differentiable data subsets, which are then each fed into N deep feedforward neural network (DNN) base learners. The final ensemble output through weighted averaging is less susceptible to error from weight initialization variation than single models, ensuring greater prediction accuracy.

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Section: Integration Of Ensemblementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Ensemble Method for Li-ion Battery State-of-Charge Estimation

Shenrun¹,

Zhang²

2022

Preprint

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show abstract

“…The tracking effect of the filtering algorithm is excellent, has gradually become the main direction of the SOC estimation method. 26 At present, for the strong nonlinearity of Li-ion batteries, 27 the SOH researches is mainly divided into two categories: mathematical-model-driven and datadriven methods. 28 These two broad categories of methods contain more than a dozen kinds of ways.…”

Section: Introductionmentioning

confidence: 99%

“…Jiang Cong et al 25 present a novel adaptive square root extended Kalman filtering algorithm that can solve the problem of filtering divergence caused by computer rounding errors, and the accuracy of estimating SOC is improved greatly. The tracking effect of the filtering algorithm is excellent, has gradually become the main direction of the SOC estimation method 26 …”

Section: Introductionmentioning

confidence: 99%

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

Ren

Wang

Chen

et al. 2022

Intl J of Energy Research

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Summary To accurately evaluate the state of charge (SOC) and state of health (SOH) of Li‐ion battery, the second‐order RC equivalent‐circuit model is used to characterize the battery performance, a novel dual adaptive Kalman filtering algorithm is presented by adding double cycles and noise adaptive steps to realize the joint estimation of the SOC and internal resistance. The state variables can be corrected with each other as go through the cycle under three operating conditions. The accuracy of the SOC estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering and the unscented Kalman filtering algorithm. Under three operating conditions, the average error and the maximum error decreased obviously. An equation for calculating the SOH in terms of internal resistance increase was built. The estimation result of the SOH effectively simulated the actual situation, compared with the actual result, the maximum error under the three operating conditions are within a lower level than the improved unscented Kalman filtering algorithm. The convergence effect of the algorithm has obvious advantages over that of the algorithm used for comparison, which could effectively track the state change of the battery.

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A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Zheng,

Huang,

Liu

et al. 2024

Advcd Theory and Sims

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As lithium‐ion batteries are the main power source of new energy vehicles, making accurate predictions of unknown State of Charge (SOC) during vehicle operation for vehicle data monitoring is vital to the advancement of intelligent new energy vehicles. In this manuscript, an expression tree‐based genetic programming regression model (ETGPR) is proposed to estimate the real‐time SOC of lithium‐ion batteries. The proposed model mainly adopts the symbolic regression technique. In addition to the current–voltage curves being fed into the model, an additional approach is designed to ensure real‐time model predictions in dynamic situations, which includes the previous moment's power in the input parameters. Different seed hyperparameters in the model are set, and the model automatically performs evolutionary calculations. Subsequently, each parameter of the model is optimally adjusted to obtain a set of regression expressions that accurately reflect the relationship between the SOC and each parameter after a specified number of iterations. Finally, the generated expression is proven to perform better in terms of its ability to capture the nonlinear relationship between SOC and battery variables. Also, the model demonstrates excellent robustness in the presence of notable noise from input‐independent features compared to other models, a root mean square error (RMSE) of less than 0.3% and a mean absolute error (MAE) of less than 0.2% are achieved. Furthermore, the potential of the model's implement‐ability under variable temperature and real driving data conditions is verified.

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SOC estimation of lithium-ion batteries for electric vehicles based on multimode ensemble SVR

Cited by 18 publications

References 20 publications

Deep Learning-Based Ensemble Method for Li-ion Battery State-of-Charge Estimation

Deep Learning-Based Ensemble Method for Li-ion Battery State-of-Charge Estimation

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

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