Due to strict regulations and the requirement to reduce greenhouse gas emissions, electric vehicles (BEVs) are a promising mode of transportation. The lithium battery is the most important power source for an electric vehicle, but its performance and life are greatly restricted by temperature. To ensure the safety of automobile operation and alleviate mileage anxiety, it is urgent to understand the current situation and predict the development and challenge of battery thermal management system. This work reviews the existing thermal management research in five areas, including cooling and heating methods, modeling optimization, control methods, and thermal management system integration for lithium batteries. Battery thermal management types include air-based, liquid-based, PCM-based, heatpipe-based, and direct cooling. Designing a better battery thermal management system not only needs to be optimized using algorithms on the model but also it uses intelligent algorithms for precise control to achieve safety and reduce energy consumption. This work also reviews the differences in thermal management systems between square and cylindrical batteries and summarizes the development trend of modularity in battery thermal management systems.
To find a traffic assignment algorithm that is less complex than the current algorithms in terms of both methodology and implementation, the route‐based incremental equilibrium assignment (IEA) method is proposed. The IEA method iteratively performs incremental assignment and new origin‐destination (O‐D) demand extraction, where the new O‐D demand is obtained by aggregating the traffic flows from the routes whose travel times are greater than the shortest travel time. By extending the IEA method, the incremental stochastic user equilibrium and reliability‐based user equilibrium assignments are proposed to solve the stochastic user equilibrium (SUE) and reliability‐based user equilibrium (RUE) models, respectively. The traffic flow assignment and new O‐D demand extraction are carried out in terms of the route choice probability and the ratio of route flow to O‐D demand in the incremental stochastic user equilibrium assignment method; the redundant traffic flows from the non‐equilibrium routes are extracted. Unlike the IEA method, incremental reliability‐based user equilibrium assignment needs to derive both the mean and variance of the route travel time. The results from numerical experiments demonstrate the essential ideas and convergence performance of the proposed IEA algorithm and illustrate that IEA can be effectively extended to solve the SUE and RUE models.
An APU (Auxiliary Power Unit) control strategy C considering battery SOC (State of Charge), vehicle power, and battery temperature is proposed to meet the cooling requirements of the battery and the cabin.
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