Estimation of the residual capacity of sealed lead-acid batteries by neural network

Yamazaki, Tsutomu; Sakurai, K.; Muramoto, Ken-ichiro

doi:10.1109/intlec.1998.793500

Cited by 29 publications

(8 citation statements)

References 4 publications

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“…Accurate estimation of SoC is a complex task. To accomplish it, a few approaches are possible: circuit models, empirical models, statistical or artificial-intelligence-based models [8], [9], [7], [10]. Circuit models require much work for each type of battery and require nonlinear elements.…”

Section: Battery Managementmentioning

confidence: 99%

See 1 more Smart Citation

Battery Choice and Management for New-Generation Electric Vehicles

Affanni

Bellini

Franceschini

et al. 2005

IEEE Trans. Ind. Electron.

383

127

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Different types of electric vehicles (EVs) have been recently designed with the aim of solving pollution problems caused by the emission of gasoline-powered engines. Environmental problems promote the adoption of new-generation electric vehicles for urban transportation. As it is well known, one of the weakest points of electric vehicles is the battery system. Vehicle autonomy and, therefore, accurate detection of battery state of charge (SoC) together with battery expected life, i.e., battery state of health, are among the major drawbacks that prevent the introduction of electric vehicles in the consumer market. The electric scooter may provide the most feasible opportunity among EVs. They may be a replacement product for the primary-use vehicle, especially in Europe and Asia, provided that drive performance, safety, and cost issues are similar to actual engine scooters. The battery system choice is a crucial item, and thanks to an increasing emphasis on vehicle range and performance, the Li-ion battery could become a viable candidate. This paper deals with the design of a battery pack based on Li-ion technology for a prototype electric scooter with high performance and autonomy. The adopted battery system is composed of a suitable number of cells series connected, featuring a high voltage level. Therefore, cell equalization and monitoring need to be provided. Due to manufacturing asymmetries, charge and discharge cycles lead to cell unbalancing, reducing battery capacity and, depending on cell type, causing safety troubles or strongly limiting the storage capacity of the full pack. No solution is available on the market at a cheap price, because of the required voltage level and performance, therefore, a dedicated battery management system was designed, that also includes a battery SoC monitoring. The proposed solution features a high capability of energy storing in braking conditions, charge equalization, overvoltage and undervoltage protection and, obviously, SoC information in order to optimize autonomy instead of performance or vice-versa.

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Section: Battery Managementmentioning

confidence: 99%

“…into the SoC [7], [10]. As usual, the NN estimation is more accurate if the training set is large and fully representative of different operating conditions.…”

Section: Battery Managementmentioning

confidence: 99%

Battery Choice and Management for New-Generation Electric Vehicles

Affanni

Bellini

Franceschini

et al. 2005

IEEE Trans. Ind. Electron.

383

127

View full text Add to dashboard Cite

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“…All these figures illustrate that the proposed method provides highly accurate estimation of the BRC for different operating profiles of EVs. It should be noted that the APEs of the proposed ANFIS model are all within 2%, which offers a significant improvement over the APE of 10% in [15] in which the ANN model is adopted.…”

Section: Evaluation and Resultsmentioning

confidence: 85%

“…Furthermore, the necessary equipment to carry out such impedance measurement is too expensive and bulky for EVs. Different from the aforementioned methods, the application of the artificial neural network (ANN) to the estimation of the BRC under variable current discharge [14], [15] and constant current discharge [16], [17] provides a tool to deal with the above difficulties. This is due to two key features of the ANN.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles

Shen¹,

Chan²,

Lo³

et al. 2002

IEEE Trans. Ind. Electron.

102

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This paper proposes and implements a new method for the estimation of the battery residual capacity (BRC) for electric vehicles (EVs). The key of the proposed method is to model the EV battery by using the adaptive neuro-fuzzy inference system. Different operating profiles of the EV battery are investigated, including the constant current discharge and the random current discharge as well as the standard EV driving cycles in Europe, the U.S., and Japan. The estimated BRCs are directly compared with the actual BRCs, verifying the accuracy and effectiveness of the proposed modeling method. Moreover, this method can be easily implemented by a low-cost microcontroller and can readily be extended to the estimation of the BRC for other types of EV batteries.Index Terms-Adaptive neuro-fuzzy inference system, battery modeling, battery residual capacity, electric vehicles.

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“…This method requires the initial SOC, determination of the battery internal losses and precise current sensors. Artificial neural networks (NNs), have been implemented by some researchers for the SOC estimation [11][12][13][14][15]. They used in static form, that is, they trained offline to approximate the relation between SOC as the output variable and many other parameters (more than three variables), as the input variables, and then are used in SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Lithium‐ion battery state of charge estimation based on square‐root unscented Kalman filter

Gholizade‐Narm

Charkhgard

2013

IET Power Electronics

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This study represents a method for estimating the state of charge (SOC) of lithium-ion batteries using radial basis function (RBF) networks and square-root unscented Kalman filter (KF). The RBF network is trained offline by sampled data from the battery in the charging process. This type of neural network finds the non-linear relation which is required in the state-space equations. The state variables include the battery terminal voltage and the SOC, at the previous sample and the present sample, respectively. The proposed method is tested experimentally on a lithium-ion battery with 1.2 Ah capacity to estimate the actual SOC of the battery. The experimental results of the proposed method show some advantages, which include: (i) it is not very sensitive to determine, precisely, the measurement and process noise covariance matrices such as Kalman filter and (ii). It contains lower noise on the output, in comparison with Adaptive extended Kalman filter (EKF).

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Estimation of the residual capacity of sealed lead-acid batteries by neural network

Cited by 29 publications

References 4 publications

Battery Choice and Management for New-Generation Electric Vehicles

Battery Choice and Management for New-Generation Electric Vehicles

Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles

Lithium‐ion battery state of charge estimation based on square‐root unscented Kalman filter

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