A novel technique for multi-objective optimization of thermal management in battery system using hybrid Genetic algorithm and Fuzzy logic is developed. Secondly, Particle Swarm Optimization algorithm combined with Fuzzy logic is also proposed for the same. The combined algorithms and fitness function for fitness evaluation is written in-house C code. For the thermal performance fitness evaluation, realistic conjugate heat transfer condition at the battery and coolant interface is adopted. The objective functions are average Nusselt number, friction coefficient, and maximum temperature. Maximizing one causes proportional increase in another, hence to achieve a moderate condition of better Nusselt number with low pumping power cost and temperature within allowable limits, these algorithms assist in optimization. Five different independent operating parameters are selected for the Multi-objective optimization and brief results are presented. The Fuzzy logic membership functions adopted can be easily modified/selected by the user to suite the battery thermal problem at hand and to assign weight to each fitness function. The fitness function obtained using the proposed multi-objective optimization technique are closer and indicate safe temperature of battery with enhanced Nusselt number and minimum friction coefficient. The maximum multi-objective fitness obtained after normalization is 0.9.

提出了一种基于混合遗传算法和模糊逻辑的电池系统热管理多目标优化技术。其次，提出了结合模糊逻辑的粒子群算法。用于适应性评估的组合算法和适应性函数是内部C代码编写的。对于热性能适应性评估，采用了电池和冷却液界面处的实际共轭传热条件。目标函数是平均努塞尔数，摩擦系数和最高温度。最大化一个导致另一个比例成比例地增加，从而在泵浦功率成本和温度在允许范围内的情况下，实现更好的Nusselt数的适度条件，这些算法有助于优化。为多目标优化选择了五个不同的独立操作参数，并给出了简要结果。用户可以轻松修改/选择采用的模糊逻辑隶属度函数，以适应手头的电池热问题，并为每个适应度函数分配权重。使用所提出的多目标优化技术获得的适应度函数更接近，并显示出安全的温度，具有增强的Nusselt数和最小的摩擦系数。归一化后获得的最大多目标适应度为0.9。用户可以轻松修改/选择采用的模糊逻辑隶属度函数，以适应手头的电池热问题，并为每个适应度函数分配权重。使用所提出的多目标优化技术获得的适应度函数更接近，并显示出具有增加的努塞尔数和最小摩擦系数的电池安全温度。归一化后获得的最大多目标适应度为0.9。用户可以轻松修改/选择采用的模糊逻辑隶属度函数，以适应手头的电池热问题，并为每个适应度函数分配权重。使用所提出的多目标优化技术获得的适应度函数更接近，并显示出安全的温度，具有增强的Nusselt数和最小的摩擦系数。归一化后获得的最大多目标适应度为0.9。