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Electric vehicles (EVs) are booming all over the world for a low carbon emission and greener environment. The fast charging is an urgent demand for consumers. However, the dramatic temperature rising during high-power charging has a high risk of trigging thermal runaway and other safety issues. This article proposes an external liquid cooling method for lithium-ion battery module with cooling plates and circulating cool equipment. A comprehensive experiment study is carried out on a battery module with up to 4C fast charging, the results show that the three-side cooling plates layout with low coolant temperature provides better temperature control effect, while the coolant velocity and type have very weak impact. Then the simulation model with high precision is built for ultra-fast charging, and a safe charging range under 5C charging is proposed. The safe charging range under 5C can be increased by 50.1%. The environment close to the room temperature demonstrates the best effects of external cooling. For actual applications in the EVs, the circulating cooling equipment outside EVs can be integrated with high-power charging infrastructure and provide a 117 km improvement in mileage under 5C safe charging which indicates a tremendous application prospect. [ABSTRACT FROM AUTHOR]

Extreme ambient temperatures cause electric vehicles’ batteries to deteriorate and have a major impact on driving range, which is a barrier for mass production of electric vehicles. Recently, much research on the optimized temperature of the battery and energy management of an electric vehicle has been conducted, but the vehicle-level optimization (i.e., vehicle speed and position optimizations) has not yet been considered together. This paper proposes a hierarchical model predictive control structure for vehicle-level and electric powertrain-level optimizations simultaneously. Specifically, using vehicle communication technologies to forecast future traffic, the required vehicle traction power coupled with battery dynamics can be predicted, and this predicted traction power is used when designing the thermal control of the battery. Furthermore, the computationally tractable control could be designed for real-time application through decoupling the vehicle and battery dynamics. The simulation results under highway and urban driving conditions show the efficacy of our approach by comparing the battery energy consumption with that of the baseline methodology, i.e., conventional control.

A secondary loop cooling battery thermal management system is designed, and then, a phased control strategy for adjusting the compressor speed according to the battery temperature interval is proposed. On this basis, the compressor speed as the decision variable, and the energy consumption of the compressor and the aging losses of the battery are as the optimization goals, which constitute a multi-objective optimization model, and a genetic algorithm is adopted to solve it. Under different weight coefficients, the Pareto Frontier of the energy consumption of the compressor and the aging losses of the battery are established. The simulation analysis is conducted on high speed dynamic conditions at an ambient temperature of 30 °C. The effects of coolant flow rate and compressor speed on battery pack temperature rise and temperature uniformity are analyzed. The simulation results show that the energy consumption of the phased control strategy is reduced by 10.7% compared with the traditional constant compressor speed control strategy under the same conditions. Under different weight coefficients, different simulation results and control strategies can be obtained, and results show that the maximum temperature and temperature uniformity can meet the requirements. There is a contradiction between the energy consumption of compressor and the aging losses of battery, but both them are highly sensitive. According to the Pareto Frontier curve, when the weight coefficient is 0.17, a balanced control strategy can be obtained, which can reduce the battery aging losses of 61.8% by only sacrificing 9.22% of the vehicle driving mileage.

The performance and safety of batteries of electric vehicles deteriorate when the battery temperature is too low or too high. The thermal management system regulating the battery temperature consumes considerable electric energy, particularly, for cooling the battery. To maximize the vehicle driving range, the means of controlling the battery temperature should minimize the energy consumption. In this paper, stochastic model predictive control is applied to the battery-cooling controller. Effective model predictive control requires a good but simple system model with proper estimation of near-future disturbances. The components of the battery cooling system are modeled to represent the energy-consumption and heat exchange mechanism with some assumptions for simplicity. The future information is estimated using historical driving data in a stochastic sense. For real-time implementation, an unequally spaced probability distribution is introduced when designing a stochastic model of future heat generation. The proposed control method shows significantly lower energy consumption while maintaining an acceptable temperature regulation performance compared to other temperature controllers, such as a thermostat-type controller and a model predictive controller with simply assumed future information. The proposed predictive controller shows robust performance compared to typical controllers by utilizing the stochastic estimation of future information. [ABSTRACT FROM AUTHOR]

Cold weather is an important matter for electric and hybrid-electric vehicles (EV/HEVs), because the electro-chemical process is slowed at low temperatures, hence inducing a loss of power and energy. To prevent it from happening, battery packs are heated via a battery thermal management system (BTMS). However, in some cases, HEVs/EVs are not connected to the grid, so the BTMS uses energy from the battery pack, thus impacting total autonomy. Strategies exist, but must be optimized. Therefore, this paper proposes the design of a HEV strategy that is parked outside and unplugged from the electrical grid. Its objective is to find a compromise between the cost in energy contained in the battery and the aging of the cells. The optimization is based on dynamic programming and utilizes an electro-thermal battery model including aging. Simulation results are then compared with other strategies, underlining that parking outside at work or overnight without plugging in are issues with the actual strategies of common EVs/HEVs, mainly because aging as a result of cold temperatures is not considered. [ABSTRACT FROM PUBLISHER]

Battery thermal management for high-power applications such as electrical/hybrid vehicles is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. An accurate battery thermal model using a Foster network is proposed. The parameters in the Foster network, including capacitance and resistance, are extracted from computational fluid dynamics (CFD) results. The Foster network model is then shown to provide equivalent results as those from CFD under transient heat dissipation inputs. The model can be readily coupled with a battery electrical circuit model to form a complete battery system circuit model capable of predicting accurate battery temperature and the impact of temperature on battery electrical transient performance. [ABSTRACT FROM AUTHOR]