Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection algorithm is used to estimate the model parameters. Then the state of health (SOH) estimator is triggered weekly or semi-monthly offline to update capacity based on the three-dimensional response surface open circuit voltage model and particle swarm optimization algorithm for accurate online SOC and state of power (SOP) estimation. At last, the Unscented Kalman Filter utilizes the estimated model parameters and updated capacity to estimate SOC online and the SOP estimator provides the power limitations considering SOC, current and voltage constraints, taking advantage of the information from both SOH and SOC estimators. Experiments show that the relative error of the SOH estimator is under 1% in all aging states whatever the loading profile is. The mean absolute SOC estimation error is under 1.6% even when the battery undergoes 744 aging cycles. The SOP estimator is validated by means of the calibrated battery model based on the HPPC test and its performance is ideal.

Energy management strategy (EMS) of plug-in hybrid electric vehicle (PHEV) can coordinate the efficient coupling operation between multiple power sources, which is of great significance to the improvement of fuel economy and transmission efficiency. During the actual operation, EMS needs the electric drive system operate frequently to achieve excellent vehicle performance. In this case, the temperature of insulated gate bipolar transistor (IGBT) module inevitably increases and the thermal-mechanical stress caused by temperature fluctuation will lead to failure. To solve the above problems, a model predictive control (MPC)-based EMS considering IGBT temperature is proposed. Firstly, Markov chain-based driver model is built for the prediction of future vehicle speed. Secondly, according to the thermoelectric coupling model description of the IGBT module, a predictive energy management framework is constructed with fuel economy and IGBT temperature indexes as objective functions. Then, under the MPC framework, a non-cooperative game model with engine and IGBT as participants is established, and Nash equilibrium is taken as the solution. Finally, the simulation results indicate that the proposed strategy not only improves the fuel economy by 13.88% and 13.48% compared with the rule-based strategy under two driving conditions, but also effectively reduces the temperature of IGBT.

While traditional one-dimensional and three-dimensional numerical simulation techniques require a lot of tests and time, emerging Machine Learning (ML) methods can use fewer data to obtain more information to assist in engine development. Combustion phasing is an important parameter of the spark-ignition (SI) engine, which determines the emission and power performance of the engine. In the engine calibration process, it is necessary to determine the maximum brake torque timing (MBT) for different operating conditions to obtain the best engine dynamics performance. Additionally, the determination of the combustion phasing enables the Wiebe function to predict the combustion process. Existing studies have unacceptable errors in the prediction of combustion phasing parameters. This study aimed to find a solution to reduce prediction errors, which will help to improve the calibration accuracy of the engine. In this paper, we used Support Vector Regression (SVR) to reconstruct the mapping relationship between engine inputs and responses, with the hyperparametric optimization method Gray Wolf Optimization (GWO) algorithm. We chose the engine speed, load, and spark timing as engine inputs. Combustion phasing parameters were selected as engine responses. After machine learning training, we found that the prediction accuracy of the SVR model was high, and the R² of CA10−ST, CA50, CA90, and DOC were all close to 1. The RMSE of these indicators were close to 0. Consequently, SVR can be applied to the prediction of combustion phasing in SI gasoline engines and can provide some reference for combustion phasing control.

The performance of an integrated thermal management system significantly influences the stability of special-purpose vehicles; thus, enhancing the heat transfer of the radiator is of great significance. Common research methods for radiators include fluid mechanics numerical simulations and experimental measurements, both of which are time-consuming and expensive. Applying the surrogate model to the analysis of the flow and heat transfer in louvered fins can effectively reduce the computational cost and obtain more data. A simplified louvered-fin heat transfer unit was established, and computational fluid dynamics (CFD) simulations were conducted to obtain the flow and heat transfer characteristics of the geometric structure. A three-factor and six-level orthogonal design was established with three structural parameters: angle θ , length a , and pitch L _p of the louvered fins. The results of the orthogonal design were subjected to a range analysis, and the effects of the three parameters θ , a , and L _p on the j , f , and JF factors were obtained. Accordingly, a proxy model of the heat transfer performance for louvered fins was established based on the artificial neural network algorithm, and the model was trained with the data obtained by the orthogonal design. Finally, the fin structure with the largest JF factor was realized. Compared with the original model, the optimized model improved the heat transfer factor j by 2.87%, decreased the friction factor f by 30.4%, and increased the comprehensive factor JF by 15.7%.

A solid-state heat pump using the electrocaloric effect (ECE) provides a new idea for the future development of heat pumps. However, most of the electrocaloric (EC) heat pumps presented in the literature are low in efficiency and use at least one moving part, which significantly reduces the reliability of the heat pump and adds to its complexities. In this context, combining the positive and negative ECEs, we proposed a plate-laminar non-mobile EC heat pump adopting Gallium-based liquid metal as an intermediate medium to guarantee highly efficient heat transfer. Numerical simulation in COMSOL Multiphysics has been performed to investigate the correlation between different operating parameters and the performance of the EC heat pump. Changing the temperature span only, a COP of 8.13 and a UVHP of 746.1 $\mathrm{W}·\mathrm{d}{\mathrm{m}}^{-3}$ were obtained at a temperature span of 7 K. It was also found that the UVHP increased by 28.45% and COP increased by 25.46% after adding one layer of EC material. The electric-induced quantity of heat and cooling capacity was found to significantly affect the heating performance. The biggest heating power of 7132.7 $\mathrm{W}·\mathrm{d}{\mathrm{m}}^{-3}$ was obtained under 200 $\mathrm{M}\mathrm{V}·{\mathrm{m}}^{-1}$, and the biggest COP of 14.84 was obtained under 150 $\mathrm{M}\mathrm{V}·{\mathrm{m}}^{-1}$ at a cyclic period of 8 s. This study provides a highly efficient, non-mobile EC heat pump that employs fluid-thermal conjugated heat transfer, and exploration of the parameters makes the optimization of the heat pump possible by fine-tuning the operation parameters.