Redox-flow battery (RFB) research spans molecular design, electrolyte optimization, electrode and membrane materials, stack operation, system management, and safety analysis, making it a constrained, multi-scale, and multi-objective energy-storage R&D problem. Although large language models (LLMs) can support scientific knowledge integration and proposal generation, generic LLM reasoning remains insufficiently adaptive across innovation-oriented exploration, rule-based execution, mechanistic modeling, and system-level trade-offs. Here we introduce ChargeBD, a character-aware heterogeneous-agent reasoning framework for guided engineering in battery development. Starting from a 50-question RFB-specific task set, we construct a 500-question ESS-LLM Benchmark and define MBTI-inspired persona agents as structured cognitive-bias templates rather than psychometric instruments or representations of real personalities. DeepSeek-V3-Plus is selected as the shared base model, and 16 MBTI-inspired persona agents are evaluated to construct a persona capability matrix and a cognitive advantage matrix.

翻译：氧化还原液流电池（RFB）研究涵盖分子设计、电解液优化、电极与隔膜材料、电堆运行、系统管理及安全分析，构成一个具有约束性、多尺度与多目标的储能研发问题。尽管大语言模型（LLM）可支撑科学知识整合与方案生成，通用大语言模型推理在创新探索、规则执行、机理建模及系统级权衡等场景中仍缺乏足够的适应性。本文提出ChargeBD——一种面向电池开发引导工程的字符感知异质体智能体推理框架。基于包含50个RFB专属任务的测试集，我们构建了含500个问题的ESS-LLM基准，并将MBTI启发式人格智能体定义为结构化认知偏差模板——并非心理测量工具或真实人格的表征。选定DeepSeek-V3-Plus作为共享基座模型，评估了16种MBTI启发式人格智能体，以构建人格能力矩阵与认知优势矩阵。