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The battery thermal management system plays an important role in electric vehicles, and determines the performance and the lifespan of electric vehicles. In this paper, optimization of the heat dissipation structure of lithium-ion battery pack is investigated based on thermodynamic analyses to optimize discharge performance and ensure lithium-ion battery pack safety. First, the heat generation and heat transfer model of the lithium-ion battery cell are derived based on thermodynamic theory. Then, the thermodynamic governing equations of battery are established based on Fluent, and the model is verified by comparing simulation data and experimental data. Afterwards, the simulation model of pure electric vehicles and the model of the heat generation rate are established using ADVISOR to analyze the dynamic performance of the battery pack. Finally, the air-cooling structure of the battery pack is designed according to the temperature field distribution obtained from the Fluent simulation, which is optimized using a back propagation neural network combined with genetic algorithms for the air flow rate, air flow channel spacing between cells, and air inlet angle. Based on the results of the optimized structure, the optimized structure effectively increases the heat dissipation performance and eliminates temperature inconsistency between the cells.

An accurate state of charge (SOC) estimation depends on an accurate battery model. The influence of nonlinear and unstable interference factors makes the accurate SOC estimation difficult. To obtain an accurate battery model, a method based on the NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural network and moving window method is proposed. This paper improves the accuracy, modelling speed and robustness of SOC estimation from the following three aspects. First, to overcome the excessive reliance on the amount of data in the model training process, the NARX recurrent neural network is used to establish the battery model. NARX (nonlinear autoregressive with external input) recurrent neural network with the delay and feedback functions can keep the input and output of a previous moment and add it to the calculation of the next moment. Therefore, better estimation results are achieved using a small amount of data; second, the moving window method is used against the gradient explosion and the gradient vanishing that may occur in the NARX model training process. Third, by comparing it with other methods under different working conditions and different temperatures, the validity of the proposed model is verified. The results indicate that the proposed model has a higher accuracy and speed of the SOC estimation. The RMSE performance of the proposed model is reduced by approximately 65%, and the execution time is shortened by approximately 50%.

Lithium-ion batteries(LIBs) have been used in electric vehicles(EVs) because of its high energy density and no pollution. As one of the important parameters of battery management system(BMS), accurately estimating the state-of-charge (SOC) can ensure the energy distribution and safe use of the battery. Therefore, in order to obtain accurate SOC estimation, this paper improves the estimation accuracy of SOC from four aspects. Firstly, to overcome the dependence of the model on the internal parameters of the battery, this paper uses the least squares support vector machine (LSSVM) to establish the battery model. The current, voltage, temperature are used as input vectors to estimate the SOC. Besides, the parameters of LSSVM are determined by a grey wolf optimizer(GWO). The GWO can improve the ability of LSSVM model by finding the global optimal solution. Thirdly, in order to improve the estimation accuracy of SOC, a novel LSSVM model based on the sliding window is proposed. The SOC estimated at the previous time, together with voltage, current and temperature measured at the current time are selected as the input vectors by sliding window method to improve the SOC accuracy. Finally, the effectiveness of the proposed model is verified under different driving conditions at different temperatures by comparing with other estimators. The comparison results indicate that the SOC estimation error(MAE) can be controlled within 1%, the root mean square error (RMSE) decreases from 0.89% to 0.22%, which are verified the effectiveness and robustness of the model.

Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this article presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM.

Accurately estimating the state-of-charge (SOC) of battery is of particular importance for real-time monitoring and safety control in electric vehicles. To obtain better SOC estimation accuracy, a joint modeling method based on adaptive unscented Kalman filter(AUKF) and least-squares support vector machine(LSSVM) is proposed. This article improves the accuracy of SOC estimation from four aspects. Firstly, the nonlinear relationship between SOC, current, and voltage is established by LSSVM. Secondly, a novel voltage estimation method based on improved LSSVM is proposed. Thirdly, the measurement equation of the novel AUKF is created by the improved LSSVM. Finally, the effectiveness of the proposed model is verified under different driving conditions. The comparison results show that the model can improve the accuracy of voltage and SOC estimation, and the SOC estimation error is controlled within 2%.

Due to the low voltage and small capacity of a Li-ion battery cell, large numbers of cells are connected to construct a battery pack to satisfy the voltage and capacity requirement of the power system of an electric vehicle. Focusing on parallel and series connection mode of battery packs, the main contributions include the following. First, in order to increase the utilization rate of cells and enhance the performance of the battery pack, a method that makes the battery pack achieve their maximum initial capacity has been proposed. Second, a dynamic modeling and analysis method for the battery pack based on the equivalent circuit model has also been proposed. The results show that the battery pack in parallel and then in series has a better performance on charge/discharge capacity, efficiency, and utilization rate of cells. Third, the performance analysis and evaluation platform of the battery pack has been built, and the battery pack test results of different groups supported the conclusion of the simulation. The battery packs in parallel and then in series can achieve superior performance and greatly increase capacity and energy utilization.

Fast charging of lithium-ion batteries is an essential problem that constrains the large-scale deployment of electric vehicles. To solve this problem, a new charging strategy is proposed in this paper. Three original contributions are made in this paper: 1) development of a novel multistage constant heating rates optimization method that reduces both the charging time and charging temperature increase, with the tradeoff between the charging time and charging temperature increase analyzed using the genetic algorithm (off-line strategy); 2) reduction of the charging time via ensuring a state of charge region by balancing the charge capacity and charge time; and 3) demonstration that the proposed method can be used under different temperatures by comparing the proposed method to the average constant current constant voltage (CCCV) under different temperatures; the comparison results suggest that the charging time of proposed method is reduced by 1.9%, 5.3%, 8.56%, and 9.54% compared to the average CCCV method under ambient temperature, 10 °C, 25 °C, and 40 °C, respectively. Moreover, the proposed method temperature rise is reduced by 48.6%, 28.3%, 67.3%, and 17.9% compared to the average CCCV method under ambient temperature 10 °C, 25 °C, and 40 °C, respectively.